Wiring LLMs into real software: APIs, structured outputs, tools, and reliability.
27 articles
A tour of the reasoning research: how prompting elicits it, how researchers categorize the techniques, and why one prominent position paper says it is not reasoning at all.
Most prompting advice transfers across models. The parts that do not will quietly break your app when you switch providers. A working map of the differences.
A production case study measured fourteen prompt modifications one by one. The results are humbling: naming the model helped, few-shot examples hurt, and templates cost accuracy.
Prompt functions wrap a prompt in a name, inputs, and a rule so you can call and compose it like code. The chat-window version is dated; the idea runs everything now.
Code generation is the most-used LLM capability and the most casually prompted. A tour of the core patterns, plus the checks that catch what the model quietly gets wrong.
Cranking temperature does not make synthetic data diverse. Injecting randomness into the prompt does. Here is the technique, with code.
You can prompt a model to produce labeled examples on demand: test fixtures, eval sets, cold-start training data. Here is how to do it well and where the sharp edges are.
Your few-shot examples carry hidden biases. The distribution and order of them can nudge the model toward a label before it reads the input. Here is how to spot it and fix it.
Models hallucinate because they are built to sound coherent, not to be right. Here are three prompt-level moves that reduce made-up answers, plus where prompting stops working.
Prompt injection, prompt leaking, and jailbreaking are three different attacks with three different fixes. Here is how each one works and what defence actually holds.
Graph prompting feeds a model structure, not just prose: entities as nodes, relationships as edges. Here is what the idea is, where it comes from, and when it pays off.
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.
Examples are the most effective context you can add to a prompt, until they aren't. When few-shot beats zero-shot, how many to use, how to pick them, and where examples won't help.
A good prompt has up to four parts: instruction, context, input data, and output indicator. Here is what each does and the design habits that make them land.
Before you rewrite a prompt, check the dials around it. Temperature, top_p, max tokens, and the penalties each change your output in a specific way. Here is when to touch each.
Prompt caching can cut input cost by up to 90%, but only if you structure prompts so the cache actually hits. Here is how it works and when it pays off.
A practical playbook for making LLM apps cheaper and faster without hurting answers: where the cost and latency actually go, and the levers that work.
A full walkthrough of streaming LLM output from the provider, through your backend, into a React UI: SSE, backpressure, cancellation, and the gotchas.
Structured outputs guarantee the model's response matches your JSON schema. Here is how it works under the hood, how it relates to function calling, and the patterns that hold up in production.