146 posts tagged with "ai-engineering"

Most LLM quality problems aren't prompt problems. They're context problems.

You spend hours crafting the perfect system prompt. You add XML tags, chain-of-thought instructions, and careful persona definitions. You test it on a handful of inputs and it looks great. Then you ship it, and two weeks later you're staring at a ticket where the agent confidently told a user the wrong account balance — because it retrieved the previous user's transaction history. The model understood the instructions perfectly. It just had the wrong inputs.

This is the core distinction between prompt engineering and context engineering. Prompt engineering asks: "How should I phrase this?" Context engineering asks: "What does the model need to know right now, and how do I make sure it gets exactly that?" One is copywriting. The other is systems architecture.

One company shipped 7,949 AI agents. Fifteen percent of them worked. The rest failed silently, looped endlessly, or contradicted themselves mid-task. This is not a fringe result — enterprise analyses consistently find that 88% of AI agent projects never reach production, and 95% of generative AI pilots fail or severely underperform. The gap between a compelling demo and a reliable system is not a model problem. It is an architecture problem.

The engineers who are shipping agents that actually work have converged on a set of structural decisions that look nothing like the toy examples in framework tutorials. This post is about those decisions: where the layers are, where failures concentrate, and why the hardest problems are not about prompts.

Most teams ship their first LLM feature, get burned by a bad output in production, and then bolt on a guardrail as damage control. The result is a brittle system that blocks legitimate requests, slows down responses, and still fails on the edge cases that matter. Guardrails are worth getting right — but the naive approach will hurt you in ways you don't expect.

Here's what the tradeoffs actually look like, and how to build a guardrail layer that doesn't quietly destroy your product.

Most engineers learn prompt engineering backwards. They start with "be creative" and "think step by step," iterate on a demo until it works, then discover in production that the model is hallucinating 15% of the time and their JSON parser is throwing exceptions every few hours. The techniques that make a chatbot feel impressive are often not the ones that make a production system reliable.

After a year of shipping LLM features into real systems, here's what actually separates prompts that work from prompts that hold up under load.

The most surprising thing about LLM function calling failures in production is where they come from. Not hallucinated reasoning. Not the model picking the wrong tool. The number one cause of agent flakiness is argument construction: wrong types, missing required fields, malformed JSON, hallucinated extra fields. The model is fine. Your schema is the problem.

This is good news, because schemas are cheap to fix.

Every AI product demo looks great. The model generates something plausible, the stakeholders nod along, and everyone leaves the meeting feeling optimistic. Then the product ships, real users appear, and things start going sideways in ways nobody anticipated. The team scrambles to fix one failure mode, inadvertently creates another, and after weeks of whack-a-mole, the prompt has grown into a 2,000-token monster that nobody fully understands anymore.

The root cause is almost always the same: no evaluation system. Teams that ship reliable AI products build evals early and treat them as infrastructure, not an afterthought. Teams that stall treat evaluation as something to worry about "once the product is more mature." By then, they're already stuck.

Most AI teams hit the same wall six weeks after launch. Initial demos were impressive, the prototype shipped on time, and early users said nice things. Then the gap between "good enough to show" and "good enough to keep" becomes unavoidable. The team scrambles — tweaking prompts, swapping models, adding guardrails — and the product barely moves.

The teams that actually improve quickly share one counterintuitive habit: they spend less time on architecture and more time staring at data. Not dashboards. Not aggregate metrics. The raw, ugly, individual failures that live inside conversation logs.

This is a field guide to the practices that separate fast-moving AI teams from ones that stay stuck.

Most AI teams are measuring the wrong things, in the wrong way, with the wrong people involved. The typical evaluation setup looks like this: a 1-to-5 Likert scale, a handful of examples, and a junior engineer running the numbers. Then someone builds an LLM judge to automate it—and wonders why the whole thing feels broken six months later.

LLM-as-a-judge is a powerful pattern when done right. But "done right" is doing a lot of work in that sentence. This post is a concrete guide to building evaluators that correlate with real quality, catch real regressions, and survive contact with production.

Most teams building with LLMs today are repeating mistakes that others made a year ago. The most expensive one is mistaking the model for the product.

After a year of LLM-powered systems shipping into production — codegen tools, document processors, customer-facing assistants, internal knowledge systems — practitioners have accumulated a body of hard-won knowledge that's very different from what the hype cycle suggests. The lessons aren't about which foundation model to choose or whether RAG beats finetuning. They're about the unglamorous work of building reliable systems: how to evaluate output, how to structure workflows, when to invest in infrastructure versus when to keep iterating on prompts, and how to think about differentiation.

This is a synthesis of what that field experience actually shows.

Most teams ship RAG and call it a retrieval strategy. They chunk documents, embed them, store the vectors, and run nearest-neighbor search at query time. It works well enough in demos. In production, users start reporting that the system can't find an article they know exists, misses error codes verbatim in the docs, or returns semantically similar but factually wrong passages.

The problem isn't RAG. The problem is treating retrieval as a one-dimensional problem when it's always been multi-dimensional.

Most engineers building with LLMs share a common arc: a working demo in two days, production chaos six weeks later. The technology behaves differently under real load, with real users, against real data. The lessons that emerge aren't philosophical—they're operational.

After watching teams across companies ship (and sometimes abandon) LLM-powered products, a handful of patterns appear again and again. These aren't edge cases. They're the default experience.

Most LLM demos work. Most LLM applications in production don't—at least not reliably. The gap between a compelling prototype and something that survives real user traffic is wider than any other software category I've worked with, and the failures are rarely where you expect them.

This is a guide to the parts that break: cost, consistency, composition, and evaluation. Not theory—the concrete problems that cause teams to quietly shelve projects three months after their first successful demo.