Getting Started with Prompt Chaining

What is Prompt Chaining

Prompt chaining is a method of using LLMs such as GPT or Claude to accomplish a task by breaking it into multiple smaller prompts and passing the output of one prompt as the input to the next. It simplifies complex tasks and streamlines the interaction with the AI model.

Prompt chaining is like assembling a series of building blocks to construct a complete solution. Instead of overwhelming the LLM instance with a single detailed prompt, we can guide it through multiple steps, making the process more efficient and effective.

Advantages of Prompt Chaining

Simplified Instructions

One of the primary advantages of prompt chaining is that it allows us to write less complicated instructions. Instead of trying to express a complex task in a single prompt, we can break it down into smaller, more straightforward steps. This simplification not only makes it easier for us to communicate with the LLM instance but also increases the chances of getting accurate results.

Focused Troubleshooting

Prompt chaining also enables us to isolate parts of a problem that the LLM might have difficulty with. If we encounter issues or inaccuracies in the responses, we can pinpoint the specific prompt in the chain that needs adjustment. This focused troubleshooting makes it easier to improve the overall quality of the output.

Incremental Validation

Another benefit of prompt chaining is the ability to check the LLM's output in stages, rather than waiting until the end. This incremental validation allows us to assess the correctness of responses as we progress through the prompts. If something goes wrong early in the chain, we can address it immediately, avoiding wasted time on subsequent steps.

Prompt chaining simplifies instructions, enhances troubleshooting, and provides incremental validation, making it a powerful technique for accomplishing complex tasks efficiently.

Use Cases of Prompt Chaining

Prompt chaining shines when applied to multi-step processes that involve both creativity and logic. By breaking down complex tasks into discrete prompts, this technique enables AIs to produce higher quality results. To illustrate the versatility of prompt chaining, here are real-world examples we've worked on with clients where it improved outcomes:

• Answering questions using documents or "chatting" with documents
• Response validation: Allows AI to double-check and refine previous outputs
• Parallel tasks: PermitsPrompt chaining is a powerful technique for optimizing AI assistant performance on complex tasks. 
• Writing long-form content like articles or stories: Break the writing process into outlined sections or chapters that the AI can expand upon in sequence.
• Research projects: Prompt the AI to 1) find source documents, 2) extract key facts/data, and 3) synthesize conclusions based on the research.
• Data analysis: Prompt the AI to 1) import datasets, 2) clean and process data, 3) run analyses, 4) generate charts/graphs, 5) interpret and summarize findings.
• Computer programming: Decompose tasks like 1) outline program logic, 2) write pseudocode, 3) translate into actual code, 4) debug/troubleshoot errors.
• Travel planning: Prompt to 1) suggest destinations based on criteria, 2) find flights/hotels, 3) build daily itinerary, 4) generate packing list.
• Job recruiting: Prompt to 1) source resumes, 2) screen candidates, 3) schedule interviews, 4) make hiring recommendations.
• Customer service: Prompt to 1) analyze ticket, 2) research issue, 3) draft response, 4) validate resolution meets needs.

The key is identifying distinct sub-tasks that can feed into each other to accomplish larger goals. Prompt chaining allows dividing complex workflows into logical, iterative steps for improved AI performance.

We will discuss these use cases and what the prompt chains may look like in another section, but first lets look at how to build prompt chains in the next section.

Building a Structured Prompt Chain

Let us dive deeper into the practical aspect of using prompt chaining by understanding how to build an effective prompt chain.

Prompt chains are a powerful way to guide the LLM through complex tasks while maintaining clarity and efficiency. By the end of this lesson, you will have a clear grasp of the essential steps in creating a prompt chain.

Step 0: Create a Prompt Recipes and Add to Library

Before constructing the full prompt chain, first create reusable prompt recipes that capture the essence of each step or task. Review best practices for writing prompt recipes. Then organize these recipes in a library for easy access later. The prompt library and recipes should be updated accordingly after each of the next steps.

Review this article on prompt construction and this article on prompt recipes.

Unlock the Power of AI with Effective Prompts

Discover the importance of prompts in AI content creation and how they can transform your output. Dive into the world of prompting and create amazing AI interactions

Prompt EngineeringSunil Ramlochan - Enterprise AI Specialist

Master Prompt Engineering: Prompt Recipes - A Framework for Reusable AI Prompts

Discover the power of prompt recipes for AI tasks! Optimise ANY prompt. Download the free prompt recipe template and take your AI game to the next level.

Prompt EngineeringSunil Ramlochan - Enterprise AI Specialist

Step 1: Identify the Main Prompts

The first step in building a prompt chain is to identify the primary prompts or steps required to accomplish your task. These prompts act as the building blocks of your chain. It's essential to break down your task into smaller, more manageable parts.

Here's what you need to do:

• Determine the sequence of tasks: Decide on the order in which each prompt should be executed. Consider what needs to happen first, second, and so on.
• Clarify the purpose of each prompt: Each prompt should have a specific purpose or objective. Be clear about what you want to achieve with each step.
• Plan for inputs and outputs: Think about what information you need to provide as input to each prompt and what information you expect as output. This clarity ensures that the prompts work seamlessly together.

Step 2: Define Input and Output for Each Prompt

Once you've identified your main prompts, the next crucial step is to define the input and output for each of them. This step ensures that the LLM receives the necessary information to perform each task correctly.

• Input for each prompt: Specify the information or data you will provide to the LLM as input. This can include text, data, context, or any relevant details.
• Output for each prompt: Clearly outline the expected output or response you anticipate from the LLM. What should the AI provide as a result of executing the prompt?
• Consider chaining compatibility: Ensure that the output of one prompt aligns with the input requirements of the subsequent prompt in the chain. Compatibility between prompts is key to a smooth flow.

Step 3: Execute the Prompt Chain

With your prompts defined and inputs and outputs clarified, it's time to execute the prompt chain. This involves sequentially feeding the output of one prompt into the input of the next prompt in the chain. Here's how to do it:

• Start with the first prompt: Begin by executing the initial prompt in your chain. This sets the process in motion.
• Capture the response: Once the LLM provides a response, capture it accurately, as it will be used as input for the next prompt.
• Feed into the next prompt: Take the response from the previous prompt and use it as the input for the next prompt in the sequence.
• Repeat until completion: Continue this process, moving through each prompt in the chain, until you have accomplished the entire task.

By following these steps, you can effectively create and execute a prompt chain that guides the LLM through a complex task while maintaining clarity and efficiency.

Prompt chaining is a valuable skill that allows you to harness the full potential of LLMs like Claude and GPT in various scenarios. In the next lessons, we will explore practical applications and best practices for prompt chaining.

Detailed Example #1: Information Extraction and Follow up

Prompt 1: Extract Direct Document Quotes Relevant to a Question

Human: Here is a document, in <document></document> XML tags:

<document>
In the heart of the forest, where the ancient trees whisper their secrets, lies a hidden treasure. It is said that the treasure can only be found by those who truly understand the language of the forest.
</document>

Please extract, word-for-word, any quotes relevant to the question "How can one find the hidden treasure in the forest?" Please enclose the full list of quotes in <quotes></quotes> XML tags. If there are no quotes in this document that seem relevant to this question, please say "I can't find any relevant quotes."

Assistant:
<quotes>
"It is said that the treasure can only be found by those who truly understand the language of the forest."
</quotes>

Prompt 2: Use Document and Quotes to Answer a Question

Human: I want you to use the document and relevant quotes from the document to answer the question "How can one find the hidden treasure in the forest?"

Here is the document, in <document></document> XML tags:
<document>
In the heart of the forest, where the ancient trees whisper their secrets, lies a hidden treasure. It is said that the treasure can only be found by those who truly understand the language of the forest.
</document>

Here are direct quotes from the document that are most relevant to the question "How can one find the hidden treasure in the forest?":
<quotes>
"It is said that the treasure can only be found by those who truly understand the language of the forest."
</quotes>

Please use these to construct an answer to the question "How can one find the hidden treasure in the forest?" as though you were answering the question directly. Ensure that your answer is accurate and doesn't contain any information not directly supported by the document or the quotes.

Assistant:
To find the hidden treasure in the forest, one must possess a deep understanding of the language of the forest. According to the document, it is said that the treasure can only be found by those who truly understand the language of the forest. Therefore, to uncover this hidden gem, you must listen to the ancient trees as they whisper their secrets and connect with the heart of the forest in a way that reveals its mysteries.

Detailed Example #2: Review and Dillegence

Prompt 1: First Pass at Identifying Grammar Errors

Human: Here is an article, contained in <article> tags:

<article>
The quick brown fox jumps over the lazy dog.
</article>

Please identify any grammatical errors in the article. Please only respond with the list of errors, and nothing else. If there are no grammatical errors, say "There are no errors."

Assistant:
..

Prompt 2: Second Pass, Passing in Errors Identified With Prompt 1

Human: Here is an article, contained in <article> tags:

<article>
The quick brown fox jumps over the lazy dog.
</article>

Please identify any grammatical errors in the article that are missing from the following list:
<list>
</list>

If there are no errors in the article that are missing from the list, say "There are no additional errors."

Assistant:
..

In this example, Prompt 1 is used to identify any grammatical errors in the given article. Since there are no errors found, the response from Prompt 1 is "There are no errors." In Prompt 2, we validate the response by asking if there are any errors missing from the list. Since the response from Prompt 1 confirmed that there were no errors, the assistant in Prompt 2 also confirms this by saying, "There are no additional errors." This demonstrates how prompt chaining can be used for response validation and extra diligence in tasks like grammar error identification.

More Practical Application and Scenarios of Prompt Chaining

Let us explore how to use prompt chaining in a real-world scenario - automating grammar error identification. Grammar errors are common in writing, and reviewing a large amount of text for such errors manually can be time-consuming. We'll demonstrate how prompt chaining can streamline this process efficiently.

Scenario #1: AI-Assisted Legal Case Preparation

Prompt chaining could help lawyers thoroughly evaluate cases and strategy by:

1. Ingesting case files, background, and evidence
2. Researching relevant laws, precedents, and past rulings related to the case details
3. Assessing the relative strengths and weaknesses of the case based on the law research
4. Outlining arguments to emphasize the strongest points and mitigate the weaknesses
5. Drafting the full legal briefs and motions incorporating the prepared arguments

Chaining these prompts would provide structure to leverage AI for robust case analysis and preparation support. The lawyer maintains full agency over case strategy and writing.

For lawyers, this technique could surface insightful arguments and precedents they may have initially overlooked. Prompt chaining lends rigor to the AI assistant's analysis process.

Here is a simplified overview of the prompt chain in the case:

Here is an example prompt chain for the AI-Assisted Legal Case Preparation use case:

Prompt 1:

Analyze the case details in these documents: <case_documents>
Based on this information, please list any relevant laws, precedents, and past rulings that could pertain to this case.

Prompt 2:

Here are the key details of the case: <case_summary>

Here is the relevant legal information identified: <legal_info_from_prompt1>

Please assess the relative strengths and weaknesses of the case based on applying the legal information to the case details.

Prompt 3:

Here is the case summary: <case_summary>

Here is the analysis of the case's strengths and weaknesses: <analysis_from_prompt2>

Please outline high-level arguments for our legal briefs and motions that maximize the strengths and minimize the weaknesses.

Prompt 4:

Here are the proposed arguments to include: <arguments_from_prompt3>

Please draft the complete legal briefs and motions for this case using the outlined arguments.

This chains the process from research to final documents, with opportunities for lawyer feedback and guidance between each prompt. The AI is focused on augmenting the lawyer's expertise throughout.

Scenario #2: AI-Assisted Medical Diagnosis

Prompt chaining could allow an AI system to help doctors methodically work through diagnosing patient conditions:

1. Take in the patient's symptoms, medical history, and test results
2. Cross-reference the patient's case details against medical databases to identify potential conditions that match
3. For each identified potential condition, assess how well the patient's specifics align to typical presentations
4. Evaluate which diagnostic possibility is most likely based on all evidence
5. Provide the doctor with the top recommended diagnosis and highlight the supporting case evidence

Chaining these prompts would walk the AI through a structured diagnostic approach, mitigating the risk of overlooking key information. The doctor maintains supervision and responsibility for final diagnosis and treatment decisions.

For doctors, this technique could surface possible diagnoses they may not have initially considered. Prompt chaining lends diagnostic rigor and completeness to the AI assistant.

Here is an example prompt chain for the AI-Assisted Medical Diagnosis scenario:

Prompt 1:

Analyze the patient's symptoms, medical history, and test results in this file: <patient_health_records>

Based on this information, please list any possible conditions that could match or explain the symptoms.

Prompt 2:

Here are the patient's details: <patient_summary>

Here are the possible matching conditions you identified: <conditions_from_prompt1>

Please assess how strongly each condition aligns with the specifics of the patient's presentation and test results. Rank them from most to least likely.

Prompt 3:

Here is the patient information: <patient_summary>

Here is the ranked list of possible diagnoses: <ranked_list_from_prompt2>

Based on all the evidence, what is the diagnosis you would recommend as most probable for this patient and why?

Prompt 4:

Please provide the recommended diagnosis for this patient along with an overview of the supporting evidence from their case details: <patient_summary>

This chains the diagnostic process from symptom analysis through to a final diagnosis recommendation, with physician oversight possible at each step. The AI is focused on assisting the physician's expertise throughout.

Scenario #3: Optimizing Talent Recruitment

Prompt chaining could enable AI to largely automate cumbersome recruiting and hiring processes. The AI assistant could:

1. Source promising resumes based on required credentials and skills for the role
2. Screen candidates to identify the most qualified applicants per the provided criteria
3. Coordinate scheduling candidate interviews based on availability
4. Make data-driven hiring recommendations by comparing candidates

Chaining these prompts would permit an AI to handle the end-to-end talent recruitment process with minimal human oversight needed.

The AI could filter applicants, determine best fits, coordinate logistics, and provide actionable hiring advice. Prompt chaining lends structure to these intricate sub-tasks.

For recruiting teams overwhelmed by manual workflows, these AI efficiencies would allow focusing on higher-impact initiatives. Prompt chained automation could become a competitive advantage.

Here is an example prompt chain for the Optimizing Talent Recruitment use case:

Prompt 1:

Review this job description: <job_description>

Please search our resume database and identify candidates whose skills and experience closely match the required and preferred qualifications.

Prompt 2:

Here are resumes of the top matching candidates: <resumes_from_prompt1>

Please evaluate each candidate in detail and rank them in order from best qualified to least qualified for this role based on the job description.

Prompt 3:

Here is the ranked list of qualified candidates: <ranked_list_from_prompt2>

Please coordinate scheduling 45 minute introductory video interview meetings for the top 5 candidates based on their listed availabilities.

Prompt 4:

Here are the interview notes and feedback for each of the top candidates: <interview_notes>

Based on their qualifications, experience, and interview performance, please provide your hiring recommendation on which candidate we should make an offer to. Explain your rationale.

This breaks down the hiring process into discrete steps that automate administrative work while focusing human review on key assessments and decisions. The AI acts as an recruiting assistant.

Scenario #4: Automating Grammar Error Identification

Imagine you have a substantial amount of text, and you want to automate the process of identifying and listing grammar errors within it. Instead of manually proofreading every sentence, you can use prompt chaining to instruct the LLM to assist in this task.

Step-by-Step Demonstration of Creating and Executing a Prompt Chain:

Step 1: Define the Main Prompts

1. Prompt 1: Initial Text Input
   • You provide the LLM with the text you want to analyze for grammar errors.
   • This is the starting point of the prompt chain, where you introduce the text.
2. Prompt 2: Identify Grammar Errors
   • You instruct the LLM to identify any grammatical errors within the provided text.
   • This is the core prompt responsible for grammar error detection.

Step 2: Define Input and Output for Each Prompt

1. Input for Prompt 1:
   • The text you want to analyze.
2. Output for Prompt 1:
   • A response that contains the text for analysis.
3. Input for Prompt 2:
   • The text from Prompt 1 (the output of the previous prompt).
4. Output for Prompt 2:
   • A list of identified grammar errors within the text.

Step 3: Execute the Prompt Chain

Now, let's put the prompt chain into action:

1. Execute Prompt 1: Initial Text Input
   • Provide the LLM with the text you want to analyze. For instance, you might input: "Here is an article with some grammar errors: 'The quick brown fox jumps over the lazy dog.'"
2. Capture the Response from Prompt 1
   • The LLM will return the same text you provided.
3. Execute Prompt 2: Identify Grammar Errors
   • Use the output from Prompt 1 as input for Prompt 2.
   • The instruction to the LLM for this prompt can be: "Please identify any grammatical errors in the text: '{{TEXT}}'" (where '{{TEXT}}' is replaced with the output from Prompt 1).
4. Capture the Response from Prompt 2
   • The LLM will respond with a list of identified grammar errors, if any.
5. Review and Correct
   • Review the list of errors provided by the LLM and make any necessary corrections to the text.
6. Repeat as Needed
   • You can repeat this process for multiple pieces of text, effectively automating the grammar error identification process.

Scenario #5: Boosting Customer Service Productivity

Prompt chaining could help customer service teams work more efficiently by automating common ticket resolution steps:

1. Analyze the customer request or issue in the ticket
2. Research the problem and existing documentation for solutions
3. Draft a customer response explaining the resolution
4. Validate that the proposed solution fully addresses the customer's needs

Chaining these sub-tasks would allow an AI agent to independently process common request types end-to-end.

The modular approach helps the AI focus on specific ticket details and documentation when identifying fixes. Prompt chaining ensures nothing gets missed in the response.

For frequent customer issues, this automation could dramatically scale support capabilities. Agents could then devote time to higher-level analysis of emerging trends and complex escalations.

Prompt chaining enables customer service teams to balance productivity gains with maintained quality and empathy.

Here is an example prompt chain for the Boosting Customer Service Productivity use case:

Prompt 1:

Read through this customer support ticket: <ticket_contents>

Please summarize the key details of the customer's issue.

Prompt 2:

Here is the summary of the customer's issue: <summary_from_prompt1>

Please search our knowledge base and identify relevant help articles that could address this problem.

Prompt 3:

Here are the customer issue details: <ticket_summary>

Here are the related help articles: <help_articles_from_prompt2>

Please draft a response email that provides the solution(s) from the help articles in an easy to understand way.

Prompt 4:

Here is the draft customer response: <draft_response_from_prompt3>

Please review the draft email and validate that it fully resolves the customer's issue based on these ticket details: <ticket_summary>

Let me know if you have any suggested revisions to better address their problem.

This structures the ticket resolution process while keeping humans in the loop to maintain tone and quality. The AI becomes a customer service assistant focusing on repetitive issue research.

Scenario #6: Answering Questions Using Documents

One effective application of prompt chaining is having an AI assistant answer questions by referencing and quoting from source documents. This can produce more accurate, evidence-based responses than having the AI summarize documents in its own words.

The first prompt extracts relevant quotes from the document based on the question. The second prompt instructs the AI to use both the quotes and document text to construct a response.

This chains the AI's own initial quote selection into its final answer, ensuring it stays grounded in the provided materials. The AI cannot infer or introduce outside information.

Prompt chaining keeps the AI focused solely on the given documents, mitigating risks of factual inaccuracy or unsupported claims. It also provides transparency, as the response clearly indicates which parts originate from the quoted materials.

For tasks involving reasoning over documents like research, analysis, and even chat, prompt chaining delivers reliable, documented AI outputs.

Here is an example prompt chain for the Answering Questions Using Documents use case:

Prompt 1:

Please read through this document: <document_content>

Based on the question "<question>", extract any relevant quotes or passages from the document that can help answer the question.

Prompt 2:

Here is the original question again: <question>

Here are the relevant quotes and passages from the document: <extracted_quotes_from_prompt1>

Please draft a short 1-2 paragraph answer to the question "<question>" that synthesizes and incorporates the relevant quotes/passages you extracted.

Prompt 3:

Here is the draft answer you generated: <draft_answer_from_prompt2>

Please review the draft answer and validate that it accurately answers the original question "<question>" solely based on the information and quotes extracted from this document: <document_content>

Make any revisions needed to correct any inaccuracies or unsupported statements.

This chains the process from document analysis through to answer finalization, with opportunities for human validation at each stage. The AI is focused on grounding its response directly in the source text.

Scenario #7: Response Validation and Refinement

Prompt chaining enables AI systems to iteratively improve their own outputs through validation and refinement prompts.

For example, a first prompt may ask the AI to review a document and identify any grammatical errors. The AI's initial list of errors can then be fed into a second prompt asking it to check if any errors are missing from that list.

If the second pass prompt uncovers additional errors, they can be added to the list and fed back into the process for another round of validation. This cyclic prompting zeroes in on any lingering issues.

The key is the AI bases later steps strictly on earlier responses, not external information. This concentrates its focus on double-checking and perfecting its prior work.

Response validation via prompt chaining minimizes mistakes that could otherwise compound across long workflows. It also provides transparency into the AI's self-corrections, building trust.

Here is an example prompt chain for the Response Validation and Refinement use case:

Prompt 1:

Please review this article draft and identify any spelling, grammar, or factual errors: <article_draft>

Prompt 2:

Here are the errors identified in the first review: <errors_from_prompt1>

Please re-review the article draft again and list any additional errors not caught the first time: <article_draft>

Prompt 3:

Here is the article draft: <article_draft>

Here are all errors identified in the two reviews: <all_errors_from_prompts1&2>

Please revise the article draft to correct all of these errors.

Prompt 4:

Here is the revised article with corrections: <revised_article_from_prompt3>

As a final check, please carefully re-review the revised article draft and confirm that all identified errors have been properly corrected.

This chains iterative checks and refinements, providing opportunities for the AI to fix any errors or issues missed in previous passes. The key is focusing the AI on validating and improving its own prior work.

Scenario #8: Enabling Parallel Processing

Prompt chaining unlocks parallel processing for scenarios with related subtasks. This can greatly accelerate complex workflows.

For example, say the goal is to generate student reading materials on a topic for 1st, 8th, and 11th grade levels. Prompt chaining permits a parallelized approach.

First, separate but identical prompts instruct the AI to outline explanations suitable for each grade level. These prompts run simultaneously to produce outlines tailored to different audiences.

Next, another set of parallel prompts has the AI expand each outline into full readable content by grade level.

This chains the AI's own initial outlines into final drafts optimized for different reading levels in parallel.

By prompt chaining, redundant subtasks can be run concurrently rather than sequentially, dramatically speeding up certain workflows. The AI still benefits from the decomposition of work into logical steps.

Parallel prompt chaining enables efficient large-scale content generation, research, data processing, and other complex multi-step AI applications.

Here is an example prompt chain to enable parallel processing:

Prompt 1:

Please generate a [data analysis, content summary, trip itinerary] for the [beginner, intermediate, advanced] level given this [dataset, article, destination preference].

<Run Prompt 1 in parallel for each level>

Prompt 2:

Here is the [outline, draft, high-level plan] you generated for the [beginner, intermediate, advanced] level: <output_from_Prompt1>

Please expand this into a full [report, article, detailed itinerary] tailored for that level.

<Run Prompt 2 in parallel for each level>

This allows Prompt 1 to generate initial outputs customized for different levels in parallel.

Then Prompt 2 can use those outputs to produce full versions for each level in parallel.

The key is identifying the distinct sub-tasks that can be intelligently generated for different variants simultaneously.

This provides scalability and efficiency gains for multi-tiered content production, analysis, planning - any use case with segmented outputs.

Scenario #9: Long-form Writing and Storytelling

Prompt chaining presents a compelling new paradigm for AI-assisted generation of long-form writing and storytelling.

Rather than prompting for an entire article or story at once, the work can be decomposed into outlines and drafts. The AI generates an outline, then expands each point into a full section or chapter.

This allows the AI to focus on creating coherent overarching structure and themes first, before diving into details. Chaining from outline to draft keeps the final output aligned with the original direction.

For fiction, prompt chaining can drive plots logically from premise to conclusion. The AI can craft character notes and scene outlines to begin, then use those to expand the full narrative.

Feedback and refinement can occur at both the outline and draft stages. This gives ample control points to steer the AI's creative process.

With prompt chaining, AIs may someday generate immersive and consistent novels, screenplays, interactive fiction, and other long-form writing.

Here is an example prompt chain for long-form writing and storytelling:

Prompt 1:

Please develop a high-level chapter outline for a [novel, screenplay, interactive fiction] based on this [premise, worldbuilding context, character backgrounds].

Prompt 2:

Here is the chapter outline: <outline_from_prompt1>

Please write a 1-2 paragraph summary of each chapter expanding on the outline.

Prompt 3:

Here are the chapter summaries: <chapter_summaries_from_prompt2>

Please draft the full text for Chapter 1 based on its summary.

Prompt 4:

Here is the draft of Chapter 1: <chapter1_draft_from_prompt3>
Please provide constructive feedback on the draft's tone, character voices, pacing, and developments. What elements worked well and what could be improved?

This chains the creative process from outline to draft, allowing iteration and feedback at each stage.

Additional promptings could continue expanding each chapter draft. Separate chains could generate character notes, setting descriptions, etc. to feed the writing.

The modular approach helps scope the complexity at each step rather than overwhelming the AI with entire books outright.

Scenario #10: AI-Assisted Research Workflows

Prompt chaining presents an intriguing new potential capability - AI systems conducting their own research projects end-to-end.

The first prompt would instruct the AI assistant to search for and compile source materials relevant to the research topic. This acts as the bibliography.

A second prompt would have the AI read the sources and extract pertinent facts, data, and findings. This extracts the raw research inputs.

Finally, a third prompt would synthesize conclusions, analysis, and insights based strictly on the compiled research.

Chaining these prompts together enables fully automated AI research, while still maintaining rigor and transparency. The data trail remains clear.

This technique could augment human research, perform preparatory work to identify promising directions, or even surface new discoveries independently.

As with all prompt chaining applications, dividing the complex endeavor into logical steps is key to maximizing accuracy, efficiency, and nuance.

Simplifying Complex Prompts into Focused Ones

Let us now look at of simplifying complex prompts. As you become more proficient in using prompt chaining, you'll often encounter scenarios where a task may initially appear daunting due to its complexity.

However, by breaking it down into smaller, more focused prompts, you can make the process much more manageable and efficient.

Simplification Strategy

When faced with a complex prompt, the key is to dissect it into its essential components. Here's a strategy to help you simplify complex prompts:

1. Identify the Core Task: Begin by identifying the core task or objective within the prompt. What is the primary action you want the LLM to perform?
2. Break Down into Subtasks: Once you've identified the core task, break it down into smaller, more focused subtasks. Each subtask should be a discrete step towards achieving the core task.
3. Create Individual Prompts: For each subtask, create individual prompts. These prompts should be concise and specific, focusing on one aspect of the overall task.
4. Execute Sequentially: Execute the prompts sequentially, starting with the subtask that is most fundamental to the core task. Use the output of one prompt as input for the next in the sequence.

Example of Prompt Simplification

Let's illustrate this concept with an example:

Complex Prompt: "Write a detailed summary of the history, cultural significance, and impact of the Renaissance period in art, literature, and science."

Simplified Prompts:

1. Prompt 1: Summarize the History of the Renaissance
   • Instruct the LLM to provide a concise summary of the historical context of the Renaissance.
2. Prompt 2: Explore the Cultural Significance
   • Ask the LLM to delve into the cultural significance of the Renaissance, focusing on its impact on art and society.
3. Prompt 3: Discuss the Impact on Literature
   • Instruct the LLM to discuss the impact of the Renaissance on literature and notable literary works.
4. Prompt 4: Analyze the Impact on Science
   • Lastly, have the LLM analyze the impact of the Renaissance on science and scientific discoveries.

By simplifying the complex prompt into these smaller, more focused prompts, you can efficiently guide the LLM through the task, ensuring that each aspect is adequately addressed. This approach not only makes the task more manageable but also facilitates clearer communication with the AI model.

Benefits of using Prompt Chaining for Prompt Simplification

Simplifying complex prompts offers several benefits:

• Improved Accuracy: Smaller, focused prompts are less prone to misinterpretation, resulting in more accurate responses.
• Enhanced Control: You have better control over each subtask, allowing you to fine-tune and iterate as needed.
• Efficiency: Smaller prompts can be executed quickly, saving both time and computational resources.
• Clarity: Clear and specific prompts reduce ambiguity, making it easier to convey your intent to the LLM.

Conclusion and Recap

Recap of Prompt Chaining Concept

• Prompt Chaining Defined: Prompt chaining is a technique that involves feeding the response from one prompt into the input of another, allowing for the breakdown of complex tasks into smaller, more manageable steps.
• Advantages of Prompt Chaining: We explored the advantages of prompt chaining, which include simplifying instructions, focused troubleshooting, and incremental validation of responses.

Benefits of Using Prompt Chaining

• Enhanced Efficiency: Prompt chaining simplifies complex tasks, making them more efficient and less time-consuming. It allows you to guide the LLM through a series of smaller, focused prompts, improving overall task performance.
• Improved Accuracy: Smaller, focused prompts are less prone to errors and misinterpretations. This leads to more accurate and reliable results.
• Clearer Communication: By breaking down tasks into smaller prompts, you can convey your intent to the LLM more clearly and reduce ambiguity in instructions.
• Versatility: Prompt chaining can be applied to a wide range of scenarios, from automating tasks to handling parallel processes, making it a versatile tool for various applications.

In future modules, we will delve deeper into advanced strategies, best practices, and explore additional real-world use cases to expand your expertise in harnessing the power of prompt chaining.

If you have any questions on Prompt Chaining or any of theexamples we've highlighted here, please feel free to comment below!