How can I optimize GPT-4o-mini prompts for a YouTube chat Chrome extension?

Below are some strategies and best practices for optimizing prompts while achieving consistent, high-quality responses:

1. Preprocess and Condense the Transcript

• Summarize or Index: For very long transcripts, consider pre-summarizing or creating an index of key sections before sending them to the model. Include only the most relevant excerpts related to the query.
• Segmentation: Break the transcript into thematic sections based on timestamps or content structure. Then, selectively include only those sections that are most likely to answer common queries.

2. Modularize Your System Instructions

• Separate Concerns: Instead of having one large system message that covers everything (context, extensive rules, formatting), consider modularizing. For example, you might load a lightweight context system message for the transcript and metadata and separately load specific behavior/formatting rules in a dynamic manner depending on the user's query.
• Minimal Essential Guidance: Prioritize the rules and instructions that are absolutely essential for the response. Extra details can sometimes cause confusion or overload the model's context window.

3. Dynamic Prompt Composition

• Reuse a Template: Instead of sending the complete set of rules every time, keep a prompt template that only includes dynamic content (for example, targeted transcript snippets based on the current query). This helps in maintaining speed while reducing prompt length.
• User-Query-Based Tailoring: Analyze the user's question first to decide which parts of the stored context or which detailed rules are necessary. For example, if a query is about summarizing, you might reduce formatting instructions in favor of a stronger focus on content summarization.

4. Use Hierarchical or Chained Prompts

• Stepwise Processing: Although you want to avoid multiple iterations, consider a two-stage chain where:
  1. The first pass extracts/identifies relevant sections from the transcript.
  2. The second uses that extracted content to produce the final answer.
  Even if this is chained internally, it can help the model focus on the essentials without being overwhelmed by a huge input at once.

5. Balance Detail with Token Efficiency

• Maintain a Compact System Prompt: Fine-tune the amount of detail in your system messages. For instance:
  • Include a concise explanation of the tool's purpose.
  • Limit formatting and behavioral rules to those truly needed.
• Leverage External Processing: If possible, offload non-critical actions (like advanced formatting enforcement) to post-processing steps outside the model. This keeps the prompt lean and focused solely on content generation.

6. Experiment with Temperature and Model Parameters

• Temperature Tuning: With a low temperature (0.3), the model tends to be more deterministic. Ensure that the prompt is clear enough so that there's minimal ambiguity.
• Gradual Complexity Increase: Test the prompt complexity with shorter videos first and then gradually increase while monitoring performance. Fine-tuning your prompt based on empirical results in your streaming scenario will yield the best balance between detail and speed.

7. Consider Prompt Compression Techniques

• Token Compression Approaches: Use techniques such as condensation of text by removing redundancies. For example, rather than including the entire transcript, provide a high-level summary with key timestamped excerpts.
• Context Windows Awareness: Be aware of the token limit and set up logic to prioritize the most critical segments when approaching the limit.

By applying these strategies, you'll be better able to optimize your prompts for speed and clarity while ensuring that the model provides consistent, high-quality responses even when dealing with large inputs.