AI Skills
Evergreen guides, playbooks, and patterns built for production. Learn what works, why it works, and when it fails.
Knowledge Map
A curated learning graph for engineers: foundations → production prompting → retrieval systems. Follow the path, or jump directly to what you are building today.
Foundations
Understand tokens, context limits, probability, and failure modes.
- How LLMs Actually Work: Tokens, Context, and Probability
- Prompting Is Not Magic: What Really Changes the Output
- Why Models Hallucinate (And Why That's Expected)
- Choosing the Right Model for the Job
Recommended next: Prompt Structure Patterns for Production
Prompting for Production
Make prompts stable, testable, and safe to integrate with systems.
- Prompt Structure Patterns for Production
- Output Control with JSON and Schemas
- Debugging Bad Prompts Systematically
- Prompt Anti-patterns Engineers Fall Into
Recommended next: Why RAG Exists (And When Not to Use It)
RAG Systems
Build grounded AI with retrieval, ranking, and measurable quality.
- Why RAG Exists (And When Not to Use It)
- Chunking Strategies That Actually Work
- Retrieval Is the Hard Part
- Evaluating RAG Quality: Precision, Recall, and Faithfulness
Recommended next: Output Control with JSON and Schemas
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Evaluating RAG Quality: Precision, Recall, and Faithfulness
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.
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Retrieval Is the Hard Part
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.
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Chunking Strategies That Actually Work
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.
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Why RAG Exists (And When Not to Use It)
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.
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Prompt Anti-patterns Engineers Fall Into
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.
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Debugging Bad Prompts Systematically
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.
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Output Control with JSON and Schemas
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.
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Prompt Structure Patterns for Production
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.
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A/B Testing AI vs Existing Logic
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.
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Agent Loops and Knowing When to Stop
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.
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Bias Detection and Mitigation in LLM Systems
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.
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Building Intuition for Non-Deterministic 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.
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Caching Strategies for LLM Systems
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.
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Career Path: What AI Engineers Actually Do
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.
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Content Moderation and Safety Filters for LLM Outputs
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.
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Debugging LLM Failures in Production
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.
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Designing for AI Latency and Streaming
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.
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Designing Tools That LLMs Can Actually Use Reliably
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.
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Handling Dual Systems During AI Migration
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.
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Error Handling in Agent Systems (It's Not Like Regular Code)
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.
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Error States and Fallback UX in AI Products
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.
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Building Eval Sets That Actually Catch Problems
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.
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Handling PII and Sensitive Data in LLM Systems
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.
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Incremental AI Adoption: Start Small, Scale Safely
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.
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Latency Optimization for LLM Applications
Users expect fast responses. LLMs are inherently slow. Here's how to minimize perceived latency and keep users engaged.
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LLM-as-Judge: When It Works and When It Fails
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.
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The Mental Model Shift: Probabilistic vs Deterministic Systems
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.
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Metrics That Actually Predict User Satisfaction
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.
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Building Model Fallback and Redundancy Systems
Single AI providers fail. This article covers fallback strategies for production AI systems: model degradation hierarchies, multi-provider redundancy, and automatic retry patterns.
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Model Security and API Key Management
Leaked API keys mean unauthorized usage, massive bills, and potential data breaches. Here's how to manage credentials securely in LLM systems.
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Monitoring LLM Systems in Production (Beyond Uptime)
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.
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Multi-Agent Systems: When and How to Coordinate
One agent can solve many problems. But complex tasks sometimes need multiple agents with specialized roles. Here's when that's worth the complexity.
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Open Source vs API-Based Models: The Real Trade-offs
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.
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Progressive Disclosure: When to Show AI Is Working
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.
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Prompt Injection: What It Is and How to Defend Against It
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.
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Prompt Versioning and Deployment (Treat Prompts Like Code)
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.
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Rate Limiting and Quota Management for LLM Systems
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.
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Reducing LLM Costs Without Sacrificing Quality
LLM API bills can quickly spiral out of control. Here's how to optimize costs while maintaining the quality users expect.
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Testing LLMs Is Different (And Why Unit Tests Aren't Enough)
Traditional software testing assumes determinism. LLM outputs are probabilistic. Here's why you need a completely different approach to quality control.
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Token Usage Patterns and Optimization Techniques
Tokens are the currency of LLM systems—understanding how they work and optimizing their usage can dramatically reduce costs and improve performance.
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Building User Trust Through AI Transparency
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.
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What AI Agents Actually Are (Beyond the Hype)
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.
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What Traditional Engineers Get Wrong About AI
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.
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When to Fine-Tune vs Prompt Engineering
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.
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When to Use Smaller vs. Larger Models
Bigger models aren't always better. Smaller models are faster and cheaper. Here's how to decide which to use for each task.
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Wrapping AI with Deterministic Guardrails
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.
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Choosing the Right Model for the Job
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.
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Why Models Hallucinate (And Why That's Expected)
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.
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Prompting Is Not Magic: What Really Changes the Output
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.
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How LLMs Actually Work: Tokens, Context, and Probability
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.