Deep explorations of agentic AI, RAG architectures, and production engineering.
64 articles
Reasoning models do the chain-of-thought for you. That flips a lot of prompting advice on its head, and getting it wrong wastes tokens and quality.
Multimodal CoT extends step-by-step reasoning to text plus vision with a two-stage split: build a rationale from both modalities, then answer from the rationale.
Chain-of-Thought lets a model reason and compute in the same breath, which is where it slips. PAL keeps the reasoning in language but hands the math to a Python interpreter.
Directional Stimulus Prompting trains a small model to produce hints that nudge a big frozen LLM toward what you want. It is prompt optimization when you cannot touch the weights.
Hand-picking chain-of-thought examples is a guess. Active-Prompt uses model disagreement to find the questions where a human annotation actually moves the needle.
ART made a frozen LLM generate its own multi-step reasoning, pause to call tools, and resume. It is 2023 research, but the pattern is everywhere in modern agents.
For questions that need world knowledge, having the model generate relevant facts first, then answer using them, beats answering cold. How it works, and when to use it over RAG.
One chain of thought can take a wrong turn. Self-consistency samples several reasoning paths for the same question and votes on the answer, beating a single greedy pass on reasoning tasks.
Telling a model to reason before it answers genuinely improves accuracy on multi-step problems. How standard and zero-shot chain-of-thought work, when to use them, and the cost.