Inference & Cost
Structured Outputs and JSON Mode: Reliable LLM Data
Learn how structured outputs and JSON mode make LLM responses reliable and parseable, with schema design, validation, and cost-aware implementation tips.
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Guides
Page 4 of 7
Inference & Cost
How to Evaluate LLM Outputs: Evals, Judges, Metrics
Learn how to evaluate LLM outputs with automated evals, LLM-as-judge patterns, and metrics that catch regressions before users do.
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Inference & Cost
LLM Quantization Explained: INT8, INT4 and Quality Loss
Learn how LLM quantization works: INT8 and INT4 precision, memory savings, speed trade-offs, and how much quality you lose when compressing model weights.
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Inference & Cost
Run LLMs Locally: Hardware, VRAM and Quantization Guide
Learn how to run LLMs on your own machine: GPU and VRAM requirements, quantization formats, and practical sizing rules for local inference.
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LLM Internals
AI Hallucinations: Why LLMs Make Things Up and How to Reduce It
Understand why LLMs hallucinate, from tokenization to alignment, and learn practical techniques to reduce made-up answers in your AI applications.
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LLM Internals
Prompt Injection Explained: Attacks and Defenses for LLM Apps
Learn how prompt injection attacks compromise LLM applications, from direct jailbreaks to indirect data poisoning, and the defenses that actually work.
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Training & Fine-tuning
RAG vs Fine-Tuning: How to Choose (With Cost Math)
Compare RAG and fine-tuning on cost, latency, and accuracy. Work through the actual math to pick the right approach for your LLM use case.
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LLM Internals
What Is MCP (Model Context Protocol)? Practical Guide
Learn what MCP (Model Context Protocol) is, how it connects LLMs to tools and data, and why it standardizes context for AI applications.
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Inference & Cost
Reasoning Models vs Standard LLMs: Hidden Token Costs
Reasoning models spend extra thinking tokens before answering. Learn how hidden token costs affect latency and pricing, and how to estimate them before you ship.
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Inference & Cost
Batch API vs Real-Time API: When Async Saves 50%
Learn when batch APIs cut LLM costs by 50% versus real-time APIs, how async throughput changes latency tradeoffs, and how to estimate savings per workload.
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Inference & Cost
LLM Context Window Explained: Why 1M Tokens Isn't Free
Understand how context windows and KV-cache make long prompts expensive, and why a 1M-token window still costs real memory, latency, and money.
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Inference & Cost
Prompt Caching Explained: Slash LLM API Costs by 90%
Learn how prompt caching reuses repeated prompt prefixes to cut LLM API costs by up to 90% and reduce latency, with practical setup and cost examples.
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