The following article outlines the capabilities and limitations of Generate through the lens of specific use cases.

Capabilities

Summarization

Use Case 1: Quickly understanding the main points of lengthy documents.

• Example Prompt: "Summarize 137-Harv.-L.-Rev-2298.pdf"
• Output: A brief summary highlighting key points as well as a list of possible follow-up questions.

Generate is capable of summarizing lengthy academic papers, legal documents, and reports. One may prompt the RAG to summarize documents as shown in the example above or by navigating to Document Search → All Sources → [Name of Source] → Summarize [Name of Source].

Use Case 2: Generate written descriptions, in flowchart fashion, that outline the main points of a document.

• Example Prompt: "Use a flowchart to depict the main points of the argument made in 137-Harv.-L.-Rev-2298.pdf"
• Output: A text-based flowchart that outlines the main points of the cited document

Generate lacks the capability to generate visual graphical representations, but it can organize text in a way that mimics simple charts or graphs.

Text Analysis

Use Case 1: Extracting key themes and topics from documents.

• Example prompt: "Identify prevalent themes in 137-Harv.-L.-Rev-2298.pdf"
• Output: A comprehensive list of themes and notable findings.

Useful for academic researchers, marketers, and analysts who need to identify and focus on recurring themes within a large text.

Use Case 2: Identifying important keywords within texts.

• Example Prompt: Perform keyword extraction on 137-Harv.-L.-Rev-2298.pdf"
• Output: Important keywords with reasoning (note: may occasionally include unrelated terms).

Useful for SEO specialists, content creators, and researchers needing to extract significant terms for indexing or further analysis.

Use Case 3: Perform sentiment analysis to assess the overall tone of a document.

• Example Prompt: "Perform sentiment analysis on 137-Harv.-L.-Rev-2298.pdf"
• Output: Sentiment distribution and tone assessment on a percentage basis: (e.g., 60% neutral, 20% positive, 20% negative).

Useful for social media analysts or PR professionals when attempting to gauge public perception of published articles or the tone of internal documents (such as emails or memo drafts).

Content Analysis

Use Case: Critiquing and providing recommendations in order to improve the content of a document.

• Example prompt: "Analyze 137-Harv.-L.-Rev-2298.pdf and offer relevant critiques/recommendations"
• Output: Critical analysis of the document as well as a few suggestions for improvement.

Comparative Analysis

Use Case 1: Comparing products, services, or companies to one another.

• Example Prompt: "Perform a comparative analysis of the Tesla Model 3 vs. BMW i4"
• Output: Detailed comparison of specified features (e.g. range, charging times, performance).

By ticking the box “Include Web Search," results are likely to be more up to date and accurate.

Use Case 2: SWOT analysis for specific business or products

• Example Prompt: “Conduct a SWOT analysis of Amazon”
• Output: Detailed comparison of specific features (Note: specific details included within a response are generally, but not always entirely, accurate).

Data Analysis

Use Case 1: Identifying trends and patterns within data.

• Example Prompt: "Analyze forestfires.csv for trends or patterns"
• Output: Identified specifics trends and patterns within the data set alongside robust explanations.

Use Case 2: Creation of predictive models based on given data.

• Example Prompt: "Conduct a predictive analysis based on forestfires.csv"
• Output: Predictive model and insights (e.g., likelihood of forest fires based on weather conditions).

Use Case 3: Detection of significant anomalies our outliers within a given dataset.

• Example Prompt: "Identify significant anomalies in forestfires.csv"
• Output: Detected anomalies as well as possible causes for these anomalies within the dataset.

Note: RAG models are not specifically designed for comprehensive dataset analysis. For such tasks, the use of data-specific tools and programming libraries generally prove more effective.

Qualitative Analysis

Use Case 1: Recurring themes within an interview transcript.

• Example Prompt: "Identify recurring themes in barack-obama-transcript.pdf"
• Output: A list of recurring themes that arose frequently in the interview transcript.

While Generate is capable of fetching a list of general themes that appear throughout a qualitative dataset, it struggles with providing quantifiable measurements of the frequency with which these themes occur.

Use Case 2: Analysis of storytelling techniques.

• Example Prompt: "Analyze storytelling elements in barack-obama-transcript.pdf"
• Output: Robust response that highlighted Obama's use of personal stories and metaphors to sway potential listeners in his favor.

Use Case 3: Analysis of language and tone.

• Example Prompt: "Examine language and tone in barack-obama-transcript.pdf"
• Output: Analysis of specific word choice, as well as the kind of response certain words might elicit.

Limitations

• Context Management: Generate sometimes struggles to maintain and correctly interpret context, leading to unrelated responses. This can result in inaccurate or irrelevant outputs if the context is not clear or shifts significantly.
• Visual Representation: Generate does not support the creation of visual flowcharts or charts directly.
• Performance with Large Datasets: Users may experience performance issues or timeouts when handling large datasets. This can limit the model's utility for extensive data analysis tasks.

Usage Guidelines

Summarization

Prompt: "Summarize [document]"

Output: Concise summary with follow-up questions.

Example: "Summarize 137-Harv.-L.-Rev-2298.pdf"

Best Practices: Provide clear and specific document references. Use follow-up questions to delve deeper into summarized content.

Theme Identification:

Prompt: "Identify prevalent themes in [document]"

Output: List of key themes and notable findings.

Best Practices: Ensure the document is relevant to the themes you are looking to extract.

Keyword Extraction:

Prompt: "Perform keyword extraction on [document]"

Output: Important keywords with reasoning.

Best Practices: Cross-reference keywords for accuracy. Be prepared to refine and verify the results.

Sentiment Analysis:

Prompt: "Perform sentiment analysis on [document]"

Output: Sentiment distribution and tone assessment.

Best Practices: Use sentiment analysis to gauge overall tone and sentiment distribution in both public and internal documents.