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
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
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
Multimodal prompting lets you send images alongside text instructions. Knowing what to ask and how to ask it determines whether you get useful or vague results.
Mahmudul Haque Qudrati
CEO & ML Engineer
Learn how to write prompts that produce accurate, concise summaries — covering length control, chain-of-density compression, source citation, and prompt differences across email, meeting, and document contexts.
Mahmudul Haque Qudrati
CEO & ML Engineer
How to escape generic AI writing — using specific stylistic constraints, exclusion lists, tone references, temperature settings, and iterative generate-critique-revise loops to get genuinely distinctive creative output.
Mahmudul Haque Qudrati
CEO & ML Engineer
Deep dives into ML algorithms, models, and applications
AI trends, techniques, and real-world implementations
How LLMs work, honest comparisons, and production usage
Claude Code, Cursor, Copilot, open-source tools reviewed honestly
Local LLMs, open models, free AI infrastructure
Fewer tokens, cheaper APIs, local alternatives with real numbers
Benchmarks explained, evaluation frameworks, model testing
LLM SEO, AI SEO, Google AI Overviews, developer marketing
iOS, Android, and cross-platform mobile app development
Modern web technologies, frameworks, and best practices
Data analysis, visualization, and engineering insights
Autonomous agents, LLM applications, and intelligent systems