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#prompt-engineering

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A cream and black mechanical keyboard with the E key lifted prominently sits on a dark wooden desk surrounded by office items and sunlight

How to structure prompts for reliable, parseable LLM output

Flaky LLM output is usually an underspecified prompt, not bad luck. Give the model a role, explicit constraints, worked examples, and a fixed format, then enforce the schema with constrained decoding, so your code parses every response the same way.

Ahmad J · Aug 11, 2026 · 4 min read

A detached mechanical keyboard key marked "E" lies on a wooden desk beside a dark keyboard

Article · aiDeep read

Prompt Caching: How It Actually Cuts Your LLM API Bill

Prompt caching isn't caching the model's answers: it's the provider reusing your prompt's processed prefix, so repeated context (system prompts, tools, documents, chat history) is billed at a fraction and skips re-processing. How it works, what it costs, and where it breaks.

Ahmad J · Aug 8, 2026 · 3 min read

Wooden compartmentalized box filled with metal type pieces arranged in divided sections, with tools blurred in background

Article · aiDeep read

Tokens, Explained: How Language Models Read Your Text and How You're Billed

Tokens are the unit of currency in every LLM interaction. Understand how text becomes tokens, why the count never matches your word count, and which levers actually control cost at scale.

Ahmad J · Aug 7, 2026 · 8 min read

Wooden desk with computer monitor, keyboard, coffee mug, and notebook in an evening office with shelves and city lights visible through windows

Article · aiDeep read

How AI Coding Assistants Actually Work Under the Hood

Transformers, context windows, retrieval versus agentic search, reasoning models, and the run-and-verify loop: the real mechanics behind Claude Code, Cursor, and Copilot, and why they succeed and fail in predictable ways.

Ahmad J · Aug 7, 2026 · 6 min read