instruction optimization

Programmatic refinement of LLM prompts to maximize accuracy while slashing token overhead.

Stop guessing with manual prompt engineering. Instruction optimization uses algorithmic compilers like Stanford’s DSPy to transform high level intent into high performing prompts. This process replaces vibe check testing with rigorous metrics. Teams using these frameworks often see 25% jumps in RAG accuracy and significant reductions in latency by stripping redundant tokens. It is the shift from brittle strings to robust, versioned code.

https://github.com/stanfordnlp/dspy