To tune generation parameters like maximum tokens, temperature, and top-p in Amazon Bedrock, focus on understanding how each impacts output quality and speed, then experiment with combinations tailored to your use case. Here’s a structured approach:
1. Adjust Maximum Tokens for Output Length and Speed Maximum tokens directly control response length. Set this to the minimum required for your task to reduce generation time. For example, a Q&A bot might need 100–200 tokens, while a summary could require 300–400. Avoid overly high values (e.g., 1000+), as they force the model to generate unnecessary text, increasing latency. If responses are truncated, incrementally increase the limit until outputs are coherent. For streaming, shorter token limits per request can improve perceived speed.
2. Use Temperature to Balance Creativity and Consistency Temperature controls randomness. Lower values (0.1–0.3) make outputs deterministic, favoring high-probability tokens—ideal for factual tasks like code generation or data extraction. Higher values (0.7–1.0) increase creativity, useful for storytelling or brainstorming. However, high temperatures can lead to irrelevant or nonsensical outputs, requiring post-processing. Temperature doesn’t directly affect computational speed but influences how many sampling steps are needed to avoid low-quality results. Start with 0.5 for general use, then adjust based on output relevance.
3. Fine-Tune Top-p for Focused Token Selection Top-p (nucleus sampling) limits token selection to a cumulative probability threshold. Lower values (e.g., 0.5–0.7) restrict choices to fewer high-probability tokens, speeding up inference and improving coherence. Higher values (0.9–0.95) allow more diversity but risk introducing less relevant tokens. For example, use top-p=0.8 with temperature=0.5 for a balance of creativity and focus. If outputs feel repetitive, slightly increase top-p. Avoid extreme combinations like high temperature + low top-p, which can produce inconsistent results.
Example Workflow
- Start with conservative defaults:
max_tokens=200,temperature=0.3,top-p=0.9. - If outputs are too generic, increase temperature to 0.6 and lower top-p to 0.7.
- If latency is high, reduce
max_tokensincrementally and test quality. - For creative tasks, try
temperature=0.8,top-p=0.95, but validate outputs for relevance.
Use Bedrock’s benchmarking tools to measure latency and evaluate outputs for task-specific quality. Adjust one parameter at a time to isolate effects. Document settings for repeatable results.