Batched Contextual Reinforcement: A Task-Scaling Law for Efficient Reasoning

Implement Batched Contextual Reinforcement (BCR) to optimize LLM Chain-of-Thought reasoning. Group multiple tasks into single prompts to reduce token consumption and inference costs, while maintaining or improving reasoning quality through shared context.

intermediate30 min6 steps

The play

Identify Batchable Reasoning Tasks
Analyze your LLM use cases. Group tasks that are similar in nature, share common data, or can benefit from processing together in a single API call.
Design Batched Prompt Structure
Craft a single, comprehensive prompt containing multiple distinct reasoning tasks. Clearly delineate each sub-task and specify the desired output format for each within the prompt.
Incorporate Contextual Information Sharing
Strategically introduce shared context, common data, or intermediate reasoning steps into your batched prompt that multiple tasks can leverage to improve reasoning consistency and efficiency.
Execute Batched LLM Inference
Send the consolidated prompt to your chosen Large Language Model API as a single request, maximizing the utility of each API call.
Parse and Extract Individual Results
Develop a robust parsing mechanism (e.g., regex, structured JSON parsing) to accurately extract the specific answer for each individual sub-task from the LLM's single, batched response.
Evaluate and Optimize for Cost and Quality
Monitor token usage, inference costs, and the quality of reasoning for batched outputs. Iterate on your prompt design and batching strategy to maximize efficiency gains without compromising output quality.

Starter code

Please answer the following questions, clearly labeling each response:

Question 1: What is the capital of France?
Question 2: Who wrote 'Romeo and Juliet'?
Question 3: Explain the concept of photosynthesis in one sentence.