Prompt Engineering & Evaluation
Systematic prompt optimization, observability, and evaluation for LLM-based RAG systems.
Overview
This project establishes a structured framework for prompt engineering across multiple LLM tasks, including Agentic RAG (see project).
It combines:
- DSPy: programmatic prompt optimization
- Langfuse: observability, logging, and debugging
- ragas: quantitative evaluation for RAG systems
Implementation Highlights
- Prompt Templates: Structured JSON/markdown outputs, instruction tuning
- Programmatic Tuning: DSPy allows parameterized prompts with metric-guided optimization
- Monitoring: Langfuse tracks prompt execution, token usage, latency, and errors
- Evaluation: ragas evaluates faithfulness, evidence recall, and answer relevance
Techniques
- Ensemble prompts with voting / majority selection
- Self-critique and automatic revision loops
- Regression testing of prompts across LLM versions
- Cross-model validation for reliability and robustness
Outcomes
- Improved answer accuracy and faithfulness
- Reduced prompt brittleness
- Faster experimentation cycles
- Transparent, auditable behavior for production-ready LLM systems