DSPy Declarative Prompt Optimization

Replaces fragile manual prompt engineering with algorithmic compilation. Optimizers (e.g. MIPROv2, BootstrapFewShot) automatically synthesize optimal prompts and few-shot examples.

STAFF ENGINEER TAKEAWAY: Treat prompts like compiled code: define Signatures, Modules, and Metrics, letting DSPy tune the prompt strings automatically.

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More Topics in 12. AI Engineering

📚RAG Pipeline (Retrieval-Augmented Generation)

Augments LLM prompt context by retrieving semantically relevant text chunks from private vector databases before generating answers.

🚪AI Gateway & Smart Fallback Proxy

A unified reverse proxy sitting between applications and LLM providers that manages semantic caching, cost rate-limiting, and automated failover.

🤖Agentic ReAct (Reasoning + Acting)

An autonomous agent paradigm that interleaves reasoning (Thought), tool execution (Action), and observation (Observation) to solve multi-step problems.

🔎Hybrid Search & Cross-Encoder Reranking

Combines sparse keyword search (BM25) with dense semantic vector search via Reciprocal Rank Fusion (RRF), followed by a Cohere cross-encoder reranker.