Improving LLM Accuracy: Techniques Beyond Prompt Engineering

1-minute read
llm • ai • rag • full-stack

When prompts plateau, these engineering levers move accuracy more than another adjective in the system message.

Better Retrieval

Hybrid search (BM25 + vectors), rerankers (Cohere, cross-encoders), and metadata filters reduce wrong context reaching the model.

Structured Outputs

Force JSON with schemas (Zod, Pydantic, OpenAI structured outputs). Parse failures trigger retry with repair prompts.

Model Routing

Small models classify intent; large models answer hard questions. Cuts cost and reduces overconfident rambling on simple queries.

Fine-Tuning and DPO

When style and format must be exact, fine-tune on verified examples. Preference optimization (DPO) aligns outputs with human rankings.

Caching and Feedback

Cache embeddings and frequent answers. Log corrections from users and analysts into a continuous improvement dataset.

Conclusion

Accuracy is a systems problem-retrieval, validation, routing, and feedback loops beat heroic prompting alone.