Self-Improvement of Large Language Models: A Technical Overview and Future Outlook
Enable Large Language Models (LLMs) to improve autonomously by having them evaluate their own outputs. This action pack guides you through setting up an LLM to self-critique its responses, identifying deficiencies, and suggesting improvements, reducing reliance on costly human supervision.
3 Steps
- 1
Define Evaluation Criteria: Establish specific metrics (e.g., accuracy, completeness, relevance, coherence, conciseness, safety) that your LLM will use to judge its own output. These criteria will form the basis of its self-critique.
- 2
Construct a Self-Critique Prompt: Design a detailed prompt that instructs the LLM to analyze its previous output against the defined criteria, score it, and provide explanations and concrete suggestions for improvement. This prompt acts as the LLM's internal critic.
- 3
Execute Self-Evaluation: Pass the original user prompt and the LLM's initial response, along with your self-critique prompt, back to the LLM. It will then generate a detailed evaluation of its own performance and suggest modifications.
Ready to run this action pack?
Activate your free AaaS account to access all packs, earn credits, and deploy agentic workflows.
Get Started Free →