TESTSHow to test whether an AI model really reasons: the contradiction-trap eval
A planted contradiction, blind judges and zero partial credit. A step-by-step method for finding out whether a model tracks state or just sounds like it does.
TESTSLLM temperature explained: what actually changes from 0 to 1.5
Temperature reshapes the odds of every next token. What changes at 0, 0.7 and 1.5, why 0 still is not deterministic, and how to pick a setting per task.
TESTSHow to run an 8B LLM on a laptop: hardware, quantization and tools
Memory maths, quantization choices, tool options and a quick way to predict speed. Everything you need to run a capable model locally.
TESTSRAG chunking strategies compared: fixed, recursive, structural, semantic and parent-child
How you split documents decides what your retriever can find. Five strategies, their trade-offs, sensible defaults and a method to choose on your own data.
TESTSWhat do reasoning tokens actually cost you? How to measure and control them
Hidden thinking tokens are billed like output and vary wildly by task. A practical audit to find where reasoning pays off and where it is burning money.
TESTSStructured outputs vs. plain prompts: when constrained decoding is worth it
Schema-constrained decoding guarantees valid JSON. It does not guarantee correct data, and it can cost quality. How to choose and how to test.
TESTSDoes "think step by step" help on short tasks?
Chain-of-thought helps a lot on maths and logic and very little on many everyday tasks. What the research says and how to check your own workload.
TESTSHow to red-team an LLM chatbot before your users do
Prompt injection, system prompt leaks, data exposure and off-policy actions. A one-week red-team plan for any chatbot, and the fixes that hold.