200 posts tagged with "ai-engineering"

Most teams treating their GenAI stack as a model integration project eventually discover they've actually built—or need to build—a platform. The model is the easy part. The hard part is everything around it: routing queries to the right model, retrieving context reliably, filtering unsafe outputs, caching redundant calls, tracing what went wrong in a chain of five LLM calls, and keeping costs from tripling month-over-month as usage scales.

This article is about that platform layer. Not the model weights, not the prompts—the surrounding infrastructure that separates a working proof of concept from something you'd trust to serve a million users.

Most engineers treat prompts as magic words — tweak a phrase, hope it works, move on. That works fine for demos. In production, it produces a system where nobody knows why the model behaves differently on Tuesday than on Monday, and where a routine model update silently breaks three features. Prompt engineering done right is a discipline, not a ritual. This post covers the full stack: when to use each technique, what the benchmarks actually show, and where the traps are.

When GPT-4o, Claude 3.5 Sonnet, and Llama 3.1 405B all score 88–93% on MMLU, what does that number actually tell you about which model to deploy? The uncomfortable answer: almost nothing. The benchmark that once separated capable models from mediocre ones has saturated. Every frontier model aces it, yet they behave very differently in production. The gap between benchmark performance and real-world utility has never been wider, and understanding why is now essential for any engineer building on top of LLMs.

Benchmarks feel rigorous because they produce numbers. A number looks like measurement, and measurement looks like truth. But the legitimacy of a benchmark score depends entirely on the validity of what it's measuring—and that validity breaks down in ways that are rarely surfaced on leaderboards.

The first time your agent silently retries the same broken tool call three times before giving up, you realize that "just add tools" is not a production strategy. Tool calling unlocks genuine capabilities — external data, side effects, guaranteed-shape outputs — but the agentic loop that makes it work has sharp edges that don't show up in demos.

This post is about those edges: how the loop actually runs, the formatting rules that quietly destroy parallel execution, how to write tool descriptions that make the model choose correctly, and how to handle errors in a way that lets the model recover instead of spiral.

There's a pattern that plays out repeatedly when teams graduate from single-agent to multi-agent AI systems: individual agents work beautifully in isolation, but the system as a whole behaves unpredictably. The agents aren't the problem. The boundaries between them are.

Studies across production multi-agent deployments report failure rates ranging from 41% to 86.7% without formal orchestration. The most common post-mortem finding isn't "the LLM gave a bad answer" — it's "the wrong context reached the wrong agent at the wrong time." The seams between agents are where systems quietly fall apart.

A team building a support triage system switched their classification pipeline from GPT-4o to o3. Accuracy improved by 2%. Costs went up by 900%. The latency jumped from 400ms to 12 seconds. They switched back.

This is the most common story in production AI right now. Reasoning models represent a genuine capability leap — o3 solved 25% of problems on the Frontier Math benchmark when no previous model had exceeded 2%. But that capability comes with a cost and latency profile that makes them wrong for the majority of tasks in the average production system. Knowing the difference is one of the more valuable things an AI engineer can internalize right now.

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.