Advanced LLM Patterns: RAG, CoT, and Model Merging
To get the most out of LLMs, you need to treat them as reasoning engines, not just knowledge bases.
1. Chain-of-Thought (CoT)
LLMs are terrible at mental math but great at articulating steps.
- The Pattern: Force the model to “think out loud” before answering.
- Prompt: “Let’s think step by step. First, calculate the total revenue. Second, subtract the tax…”
- Why it works: It gives the transformer more tokens to compute the intermediate state before committing to a final answer.
2. Advanced RAG (Hybrid Search)
Don’t rely on vector search alone.
- Hybrid Search: Combine Keyword Search (BM25) for exact matches (like part numbers) with Vector Search for semantic concepts.
- Re-ranking: Use a Cross-Encoder to re-score the top 50 documents to find the true “Needle in the haystack.”
3. Model Merging (Franken-merges)
A new technique in the open-source community involves merging the weights of two different models (e.g., a “Math” model and a “Coding” model) without retraining.
- Technique: SLERP (Spherical Linear Interpolation).
- Benefit: You get a model that is good at both tasks without the massive cost of training from scratch.
Summary
The frontier of LLMs is shifting from “Prompt Engineering” to “System Engineering.” By combining CoT, Hybrid RAG, and custom Model Merging, you can build systems that vastly outperform raw GPT-4.
