AI Skills

Without evaluation, RAG systems cannot improve reliably. This article introduces practical metrics and evaluation strategies for measuring retrieval accuracy, answer grounding, and regression over time.

Most RAG failures stem from poor retrieval, not weak models. This article explains why retrieval is difficult, how to improve it, and how to debug retrieval failures systematically.

Effective chunking is an information architecture problem, not a text-splitting task. This article covers practical chunking strategies that improve retrieval accuracy in real-world RAG systems.

RAG is not a universal fix for AI correctness. This article explains the real problem RAG addresses, its hidden costs, and how to decide whether retrieval is justified for a given system.

Many prompt failures come from familiar engineering anti-patterns applied to natural language. This article identifies the most common prompt anti-patterns and explains why they break down in production.

When AI outputs fail, random prompt tweaking is not debugging. This article presents a systematic methodology for identifying, reproducing, and fixing prompt-related failures in production systems.

Free-form AI output is fragile in production. This article explains how to use JSON and schema validation to make LLM outputs safer, more predictable, and easier to integrate with deterministic systems.

Prompts used in production must behave like interfaces, not ad hoc text. This article introduces proven prompt structure patterns that improve reliability, debuggability, and long-term maintainability.

You cannot know if AI is better than your existing system without rigorous testing. This article covers A/B test design, metrics selection, statistical significance, and avoiding common pitfalls when comparing AI to traditional logic.

Agents iterate until they solve the problem—or until they loop forever, burning tokens and accomplishing nothing. Here's how to design termination conditions that work.

LLMs learn from internet data, which means they learn human biases too. Detecting and reducing bias isn't optional—it's essential for building fair systems.

AI engineering requires different intuition than traditional software. This article covers how to build instinct for probabilistic systems through experimentation, pattern recognition, and embracing uncertainty.

LLM API calls are slow and expensive. Caching can dramatically reduce both, but naive caching strategies fail because prompts rarely repeat exactly. Here's what works.

What does an AI engineer actually do all day? This article demystifies the role: real responsibilities, required skills, career progression, and how AI engineering differs from ML engineering and traditional software engineering.

LLMs can generate harmful, biased, or inappropriate content. Hoping they won't isn't a safety strategy. Here's how to detect and prevent problematic outputs.

When traditional code fails, stack traces tell you what went wrong. When LLMs fail, you get plausible-sounding nonsense or silence. Here's how to debug the undebugable.

AI systems are slower than traditional APIs. This article covers UX patterns that work with AI's latency characteristics: streaming responses, progressive loading, and setting accurate user expectations.

LLMs can call functions and APIs, but they'll make mistakes you'd never see from human developers. Here's how to design tool interfaces that minimize errors.

AI migration means running two systems at once for months. This article covers dual-system architecture patterns, data synchronization, cost management, and knowing when it is safe to retire the old system.

Traditional error handling assumes you can predict failure modes and write catch blocks. Agents fail in ways you can't anticipate, and they need to recover autonomously.

AI systems fail differently than traditional software. This article covers error UX patterns that help users understand, recover from, and work around AI failures without losing trust.

A good evaluation dataset isn't just random examples. It's a carefully curated collection that stresses your system where it's most likely to fail.

LLMs process user data, and that data often includes personally identifiable information. Mishandle it, and you violate regulations and lose user trust. Here's how to do it right.

Do not rip out your existing system and replace it with AI overnight. This article covers strategies for incremental AI adoption: shadow mode, low-risk features first, and progressive rollout.

Users expect fast responses. LLMs are inherently slow. Here's how to minimize perceived latency and keep users engaged.

Using one LLM to evaluate another sounds circular, but it's one of the most practical ways to scale quality assessment. Here's when it's reliable and when you need humans.

Traditional software is deterministic. AI is probabilistic. This fundamental difference requires a mental model shift that many engineers struggle with. This article covers what changes, what stays the same, and how to think about building reliable systems on unreliable foundations.

You can measure accuracy, latency, and token costs easily. But the metrics that matter most are the ones that correlate with whether users find your AI system valuable.

Single AI providers fail. This article covers fallback strategies for production AI systems: model degradation hierarchies, multi-provider redundancy, and automatic retry patterns.

Leaked API keys mean unauthorized usage, massive bills, and potential data breaches. Here's how to manage credentials securely in LLM systems.

Traditional monitoring checks if services are up and latency is acceptable. LLM monitoring needs to track whether outputs are still good—and that's much harder.

One agent can solve many problems. But complex tasks sometimes need multiple agents with specialized roles. Here's when that's worth the complexity.

Choosing between open source models and API providers is not about ideology. This article breaks down the real engineering trade-offs: infrastructure costs, deployment complexity, model updates, and vendor lock-in.

Not all AI processes should be visible to users. This article covers when to show AI's internal work, how much detail to reveal, and patterns for progressive disclosure that enhance rather than overwhelm.

Users can trick LLMs into ignoring your instructions and following theirs instead. This isn't theoretical—it's happening in production. Here's how to protect your system.

Prompts determine system behavior just like code does, but most teams manage them in ad-hoc ways. Here's how to version, test, and deploy prompts systematically.

LLM APIs have strict rate limits and token quotas. Hit them unexpectedly, and your application breaks. Here's how to stay within limits while serving users reliably.

LLM API bills can quickly spiral out of control. Here's how to optimize costs while maintaining the quality users expect.

Traditional software testing assumes determinism. LLM outputs are probabilistic. Here's why you need a completely different approach to quality control.

Tokens are the currency of LLM systems—understanding how they work and optimizing their usage can dramatically reduce costs and improve performance.

Users distrust AI systems that hide their nature or oversell capabilities. This article covers transparency patterns that build trust: disclosure, confidence indicators, and honest limitation acknowledgment.

The term 'agent' gets thrown around for everything from chatbots to autonomous systems. Here's the technical definition and why it matters for how you build.

Engineers coming from traditional software development bring assumptions that do not work for AI. This article covers the most common misconceptions, why they are wrong, and what actually works instead.

Fine-tuning is not always better than good prompting. This article provides a clear framework for deciding when to invest in fine-tuning versus when prompt engineering is sufficient.

Bigger models aren't always better. Smaller models are faster and cheaper. Here's how to decide which to use for each task.

AI is probabilistic and unpredictable. This article covers techniques for wrapping AI with deterministic guardrails: input validation, output constraints, and safety checks that prevent AI failures from reaching users.

There is no universally best AI model. This article presents a production-minded approach to model selection, focusing on trade-offs, system requirements, and strategies for switching and fallback.

Hallucination is not a bug in large language models but a predictable outcome of probabilistic text generation. This article explains why hallucinations happen, when they become more likely, and how engineers should design around them.

Prompting does not make models smarter or more truthful. This article explains what prompts actually change under the hood, why small edits cause big differences, and how engineers should think about prompting in production systems.

A production-minded explanation of what LLMs actually do under the hood—and why tokens, context windows, and probability matter for cost, latency, and reliability.