Category
Prompt Engineering
Every technique that works — with real examples
// 12 articles filed
// FIG. CAT-PROM
PristrenPristrenEvery technique that works — with real examples
// 12 articles filed
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Customer support AI fails in predictable ways. The right system prompt prevents most of them. Here are the patterns that work and the mistakes that create problems.
Mahmudul Haque Qudrati
CEO & ML Engineer
How to test prompts systematically - defining test sets and success criteria, building golden datasets for regression testing, A/B testing in production, statistical significance, and the minimum viable setup for small teams.
Mahmudul Haque Qudrati
CEO & ML Engineer
Chain of Density produces better summaries by iteratively densifying a sparse draft. Each pass adds missing information without increasing length. Here is how it works.
Mahmudul Haque Qudrati
CEO & ML Engineer
A practical guide to system prompt security - understanding extraction and injection attacks, defense layers that actually work, and the fundamental truth that system prompts cannot be cryptographically secured.
Mahmudul Haque Qudrati
CEO & ML Engineer
Prompts in production need versioning, testing, and rollback capability just like code. Here is the system that prevents silent regressions when you improve a prompt.
Mahmudul Haque Qudrati
CEO & ML Engineer
The science of choosing few-shot examples - diversity, representativeness, ambiguity, relevance, dynamic selection, negative examples, ordering effects, and the empirical 3-5 example sweet spot from Brown et al. 2020.
Mahmudul Haque Qudrati
CEO & ML Engineer
Models default to generic structures, hedging phrases, and formulaic openings. Negative prompts tell them what not to do and break those patterns effectively.
Mahmudul Haque Qudrati
CEO & ML Engineer
Compressing prompts reduces token costs without degrading output quality. These techniques can cut your prompt length by 40-60% with the same results.
Mahmudul Haque Qudrati
CEO & ML Engineer
How to write prompts that produce reliable, consistent classification labels - covering category definitions, JSON output, multi-label vs single-label, confidence scores, and when to use zero-shot vs few-shot vs fine-tuning.
Mahmudul Haque Qudrati
CEO & ML Engineer
A complete guide to extracting structured data from text with LLMs - field definitions, JSON schemas, function calling for guaranteed structure, missing field handling, batch efficiency, and accuracy limits.
Mahmudul Haque Qudrati
CEO & ML Engineer
Production AI applications need system prompts built from specific patterns: persona anchoring, format specification, knowledge boundaries, escalation, and self-correction.
Mahmudul Haque Qudrati
CEO & ML Engineer
LLMs help with research synthesis and structure but fail on specific facts and citations. Knowing the boundary determines whether AI speeds up or corrupts your research.
Mahmudul Haque Qudrati
CEO & ML Engineer
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