How to Summarise PDF with ChatGPT: Prompting Tips

In the fast-paced professional world, getting quick insights from lengthy PDFs can be a daunting task. Whether you're dealing with complex legal documents, detailed financial reports, or extensive research papers, extracting the key points swiftly is crucial. This is where AI, particularly tools like ChatGPT, can make a significant difference. By employing effective prompting techniques, you can transform time-consuming reading tasks into rapid, concise summaries. In this guide, we'll explore simple strategies to harness AI’s power, helping you navigate token limits and maintain the accuracy of summaries across various applications. By integrating these tools into your workflow, you'll work smarter and faster, freeing up time for more strategic tasks.

Understanding Basic Prompts and Their Limitations

When using ChatGPT to summarize PDFs, it's crucial to start with a good grasp of basic prompts and be aware of their limitations. This understanding will help you get more accurate and useful summaries.

Key Points:

Simple zero-shot prompts such as "Summarize this" can be effective for short texts. However, these become less effective with large PDFs due to token limits, typically around 800-1500 tokens per chunk. This means you can't input a whole PDF at once and expect meaningful results.
Inconsistent outputs and hallucinations can occur if you don't structure your prompts well. Language models like ChatGPT are sensitive to how prompts are framed, so clear and structured input is essential to get consistent results.
A common mistake is trying to paste entire PDFs into one prompt. This often leads to incomplete or jumbled summaries. Instead, it's more effective to split the document into manageable chunks and use a technique called chaining, where you connect the summaries of each chunk to form a cohesive overall summary.

Examples:

For a short text, you might simply prompt, "Summarize the following article." This works well for concise content but not for detailed reports or lengthy documents.
When dealing with a large PDF, you could break it down into sections and use a prompt like, "Please summarize the first chapter, focusing on key themes."

Mistakes to Avoid:

Avoid using overly broad or generic prompts without context. This can cause the AI to generate responses that are too vague or unrelated to your specific needs.
Don't overlook the importance of chunking. Trying to feed a long document all at once will often exceed token limits and lead to incomplete summaries.

Advanced Techniques:

Chunking and Chaining: Break the PDF into logical sections and summarize each one separately. Then, combine these summaries to create a cohesive overview.
Iterative Refinement: Begin with an initial summary and refine it by asking the model to clarify or expand on specific sections. This can help capture nuances that a basic prompt might miss.
Contextual Prompts: Provide context for each chunk, such as "Summarize the section on market trends," to guide the model in producing more relevant summaries.

By understanding these basic prompts and their limitations, and by employing some advanced techniques, you can significantly improve the quality of your PDF summaries. This approach not only saves time but also ensures you get precise, actionable insights from your documents.

Enhancing Summaries with Prompt Templates and Role-Based Prompting

When using ChatGPT to summarize PDFs, crafting the right prompts can significantly enhance the quality and relevance of the summaries. By employing prompt templates and role-based prompting, you can take more control over the output, ensuring that it meets your specific needs.

Use Standardized Templates

Standardized templates are your blueprint for consistent summaries. They help control the length, format, tone, and focus of the summary. For instance, if you need concise insights for a quick briefing, you might use a prompt like:

"Act as an executive analyst. Summarize in 3 bullet points: (1) main insight, (2) key risks, (3) recommended actions. ≤120 words."

Such templates ensure that the AI provides clear, focused information in a structured format. By specifying elements like "3 bullet points on key risks," you guide the AI to deliver exactly what you need.

Role-Based Prompting

Assigning roles to the AI adds another layer of specificity.By the way, Dmitry Baraishuk, a Software engineer and AI practitioner (Dev.to technical author), shared this prompt engineering approach on dev.to last year with some killer prompt examples. By defining a persona, such as "executive analyst" or "medical researcher," you tailor the summary to the context of the reader. Consider the following examples:

"You are an assistant helping me understand a long PDF. Summarize the following passage: Focus on main argument, key findings, and important numbers/dates."
"You are a medical researcher. Summarize for specialists: study design, outcomes, statistics, limitations."

This technique allows the AI to adopt a perspective that aligns with the needs of the intended audience, whether they are specialists or general readers.

Few-Shot Examples for Consistency

To maintain style consistency across different parts of a document, provide few-shot examples. This involves giving the AI a few instances of the desired output style, which it can use as a reference for the rest of the summaries. If you start with a few well-crafted summaries, the AI can mimic that style across various sections.

Mistakes to Avoid

Avoid overly generic prompts that leave too much to interpretation. Such prompts can lead to inconsistent or irrelevant summaries. Always include specific instructions to guide the AI effectively.

Advanced Techniques

For more nuanced summaries, consider using advanced techniques like specifying the level of detail or the type of language to be used. For instance, you might ask for a "technical summary for engineers" versus a "layperson explanation," depending on your audience.

By strategically using prompt templates and role-based prompting, you can transform ChatGPT into a powerful tool for generating tailored, insightful summaries from your PDFs.Galileo AI Team, a Product and research team at Galileo AI, an observability and evaluation platform for LLM applications, shared this prompt engineering approach on galileo.ai last year with some killer prompt examples Whether you're preparing for a meeting, conducting research, or simply staying informed, these techniques ensure that you get the most relevant and actionable information.

Implementing MapReduce and Prompt-Chaining for Large PDFs

When dealing with large PDFs, using ChatGPT effectively requires strategic approaches like MapReduce and prompt-chaining. These methods streamline the summarization process, ensuring you extract valuable insights without getting bogged down by sheer volume. Here’s how you can implement these techniques:

MapReduce Method

Key Points:

Split the PDF: Start by dividing your PDF into manageable chunks, ideally between 800 to 1500 tokens each. This helps the AI process information without losing context.
Map Phase: Summarize each chunk using a consistent template. This might involve paraphrasing sections or extracting key information. For example, if you're summarizing a report, you could focus on the main findings or conclusions.
Reduce Phase: Once you have individual summaries, combine these into broader themes to create an executive overview. This helps in distilling the content into a coherent narrative.

Mistakes to Avoid: Ensure chunks don't cut off mid-sentence or disrupt the natural flow of information, as this can lead to incomplete or misleading summaries.

Field-Extraction Chain

For documents like research papers or technical reports, a field-extraction chain can be highly effective. This technique involves:

Extracting Specific Fields: Use AI to pull structured data such as objectives, methods, and results in a JSON format per chunk.
Synthesize Information: Combine these structured data points to form a comprehensive overview.

Advanced Techniques: Customize extraction templates to match the document type, ensuring accuracy and relevance.

Few-Shot Guided Chain

To improve accuracy and maintain consistency across summaries:

Provide Examples: Start with 1-2 example summaries for your AI. These should serve as a guide for tone, style, and content focus.
Apply and Consolidate: Use these examples to process new chunks of text and then consolidate them into a unified summary.

Industry Tip: In legal or financial documents, emphasize risks and obligations. For research or medical papers, decompose content into fields like methods and results to avoid hallucinations or inaccuracies.

Mistakes to Avoid: Avoid overwhelming the AI with too many examples at once, as this can dilute its effectiveness rather than enhance it.

By leveraging these strategies, you can efficiently summarize large PDFs, ensuring the final output is not only concise but also meaningful. Implementing these techniques will help you transform complex documents into actionable insights with ease.

Advanced Strategies: Chain-of-Thought, Few-Shot, and Structured Outputs

When it comes to summarizing PDFs using ChatGPT, leveraging advanced techniques can significantly improve the accuracy and usefulness of the output.Look, ISE (Industry Solutions Engineering) Team, Microsoft, a Microsoft Industry Solutions Engineering team specializing in applied AI and LLM systems, shared this prompt engineering approach on devblogs.microsoft.com with some killer prompt examples. Here we explore strategies such as chain-of-thought reasoning, few-shot prompting, and structured outputs to enhance your summarization tasks.

Actionable Examples

To efficiently summarize a PDF, consider using structured, step-by-step prompts. For instance:

Example 1: "You are [role]. Step 1: Extract the main idea and supporting points from the text. Step 2: Synthesize these points. Output as JSON in 150 words, using only the provided text."
Example 2: "First, list the main topics covered in the document. Then, identify key points under each topic. Finally, create a 5-bullet summary and a concise conclusion."

These examples help maintain clarity and consistency in your outputs, which is crucial for professional documents.

Common Mistakes and Solutions

Avoiding pitfalls in the process is as important as the strategy itself:

Vague Prompts: These can lead to hallucinations, where the AI generates information not based on the text. The solution? Clearly specify a structure and instruct to "Base only on text, specify structure."
Inconsistent Outputs: To prevent this, use fixed templates and integrate few-shot examples to guide the model.
Audience Variability: Address differing audience needs by starting with a neutral base summary and then tailoring it with role-specific rewrites, e.g., simplifying for a patient versus detailing for a clinician.

Advanced Techniques

These techniques can elevate your summarization efforts:

Chain-of-Thought: This involves breaking down the task—Step 1: List topics, Step 2: Identify key points per topic, Step 3: Synthesize a summary. This process aids in retaining factual information.
Structured Output: Enforce formats like JSON or bullet points with specific constraints such as "use only provided text, no new claims."
Iterative Refinement: Start with a base extraction of key points, then rewrite or adjust the summary to suit different audiences. This ensures clarity and relevance.
Semantic Compression: Group similar text chunks together and summarize the unique points, keeping the output concise.

Expert Recommendations

Experts provide valuable strategies for large-scale or high-stakes summarization:

Dmitry Baraishuk's MapReduce Method: Use consistent templates for production-scale PDF summarization to enhance efficiency and accuracy.
Microsoft ISE's Structured Data Approach: In high-stakes domains, begin by extracting structured data to minimize errors before crafting a summary.

By integrating these advanced strategies into your PDF summarization tasks, you can produce more precise, tailored, and valuable outputs. This not only streamlines your workflow but also ensures that the information is presented in a clear and actionable manner.

Ready-to-Use Prompt-Chain Template for how to summarise pdf with chatgpt

Here's a prompt-chain template designed to assist you in summarizing a PDF document using ChatGPT. This template guides you through a series of connected prompts that build upon each other to extract and refine information from a PDF. You can customize these prompts to fit specific needs by adjusting the focus or depth of the summary. The expected result is a concise and coherent summary of the PDF content. Note that this template assumes you have access to a tool or service that can extract the text from the PDF before using ChatGPT.

Introduction

This prompt-chain helps you summarize a PDF document by guiding you through extracting key information and refining it into a concise summary. By following these steps, you can efficiently distill complex documents into understandable summaries. Customize the prompts to focus on particular sections or topics of interest. Be aware that the quality of the summary depends on the clarity and organization of the extracted text from the PDF.

Prompt-Chain Template

# Step 1: System Prompt
# This step sets the context for ChatGPT to act as a summarizer.
# It ensures that the model understands the task and its objective.

System:
"You are an expert document summarizer. Your task is to provide clear and concise summaries of complex documents, highlighting key points and main ideas."

# Step 2: User Prompt - Initial Text Review
# This prompt instructs the model to review the text and identify main themes.
# The extracted text from the PDF is provided in small chunks for better processing.

User:
"Review the following text extracted from a PDF document and identify the main themes and key points. Text: '[Insert extracted text here]'"

# Expected Output Example
# The model should provide a list of themes and points.
"Main Themes:
1. Theme A
2. Theme B
Key Points:
- Point 1
- Point 2"

# Step 3: User Prompt - Detailed Insights
# This prompt asks for more detailed insights on the identified themes.
# It helps in understanding the nuances and specifics of each theme.

User:
"Based on the identified themes, provide detailed insights for 'Theme A'. Include any significant findings or conclusions."

# Expected Output Example
# The model offers a deeper exploration of the theme.
"Theme A Insights:
- Insight 1
- Insight 2
- Conclusion"

# Step 4: User Prompt - Summary Compilation
# This step compiles the insights into a coherent summary.
# It emphasizes clarity and coherence in the final output.

User:
"Compile the identified themes and detailed insights into a comprehensive summary of the document."

# Expected Output Example
# A well-organized summary that combines all previous findings.
"Summary:
The document primarily discusses Theme A and Theme B. Key points include...
In-depth analysis of Theme A reveals...
Overall, the document concludes that..."

# Step 5: User Prompt - Custom Focus
# This optional step allows customization for specific sections or topics.
# It is useful for targeted summaries or specific areas of interest.

User:
"Focus on section '[Specify section or topic]' and summarize its content with special attention to [Specify details]."

# Expected Output Example
# A focused summary on the specified section or topic.
"Focused Summary:
In section '[Specified section]', the document explores...
Key details include..."

Conclusion

This prompt-chain template provides a structured approach to summarizing PDF documents using ChatGPT. By following these steps, you'll extract and refine key information, resulting in a concise and comprehensive summary. Customize the prompts to target specific sections or details as needed. Remember that the quality of the summary will depend on the clarity of the extracted text and the specificity of your prompts. Considerations include ensuring the text chunks are manageable for the model and ensuring clarity in the extracted text.

In conclusion, mastering actionable LLM prompting techniques such as MapReduce chains, role-based templates, and Chain of Thought (CoT) decomposition can significantly enhance your ability to summarize PDFs effectively using AI tools like ChatGPT. These methods provide a structured approach to extracting meaningful insights from complex documents, ensuring you maintain accuracy and relevance in your summaries.

By implementing these strategies today, you can streamline tasks such as legal reviews, financial briefs, or research analysis, boosting both productivity and accuracy without the need for complex coding skills. AI agents like ChatGPT are valuable allies that can handle the heavy lifting, allowing you to focus on decision-making and strategic planning.

We encourage you to explore these techniques and integrate them into your workflow to maximize efficiency and take full advantage of what AI has to offer. Embrace this technology to transform your document processing tasks into opportunities for greater insight and informed action.