How can I accelerate GPT-4 summary generation using incremental caching?

Below is a summary of some techniques you might consider for speeding up GPT‑4 inference in this scenario:

1. Incremental Summarization/Caching

If your user’s report has a large, unchanging prefix, you might avoid reprocessing that unchanged text on every generation. Instead:

• Cache the previous summary: When new text is added, generate a summary only for the incremental part.
• Merge summaries: Use a prompt that includes both the cached summary (covering the static part) and the new text. This lets GPT‑4 focus its heavy processing only on what has changed instead of re-summarizing the entire text.

2. Two-Stage Pipeline with a Faster Model

As detailed in latency optimization discussions:

• Precompute with a faster model: For well-defined tasks like summarization, consider running an intermediate model (for example, a fine-tuned GPT‑3.5) in the background to quickly generate an approximate summary.
• Refinement by GPT‑4: Then, when the user clicks “generate,” perform a final pass with GPT‑4 using the precomputed summary plus any new text. This leverages the faster model where possible and only engages GPT‑4 for the most critical final output.

3. Efficient Prompt Construction

Since the unchanged prefix is reused:

• Minimize token repetition: Keep your prompt lean by only including what’s necessary. If the prefix can be represented succinctly (or summarized previously), do so to save on tokens that GPT‑4 has to process.
• Precompute embeddings (if applicable): While not directly an inference speedup, reducing redundancy may allow you to work with shorter contexts that load faster.

Caveats

• Quality vs. speed trade-offs: When relying on cached summaries or a two-stage approach, some degradation in quality may occur compared to a full re-generation. Test thoroughly to ensure the final summary meets your quality criteria.
• Implementation overhead: Introducing caching or a multi-model pipeline might require additional engineering. The improvements in latency should be weighed against the cost and complexity of setup.

These approaches primarily leverage design-level optimizations by reducing redundant work—keeping the static content processed once or using faster models for parts where high-level reasoning isn’t as critical.

Hope this helps!