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
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
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
Where LLMs outperform traditional MT tools on translation tasks — tone, idioms, domain context — and where they fall short, plus practical techniques including style guides, glossaries, audience specs, and back-translation QA.
Mahmudul Haque Qudrati
CEO & ML Engineer
A practical decision framework for choosing between context stuffing and retrieval-augmented generation — covering token economics, chunking strategy, hybrid approaches, and a cost comparison between stuffing 500 pages versus retrieving 5 chunks.
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
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
How to write prompts that produce actionable code review — specifying focus areas, severity tiers, concrete fix examples, adversarial review framing, and the difference between diff review and full-file review.
Mahmudul Haque Qudrati
CEO & ML Engineer
How agent system prompts differ from chatbot prompts — tool descriptions, invocation criteria, output format for tool calls, stopping conditions, safety constraints, ReAct format, handling uncertainty, and common failure modes.
Mahmudul Haque Qudrati
CEO & ML Engineer
A research-backed examination of prompting techniques that underperform their reputation — chain-of-thought on simple tasks, longer prompts, role prompting, threats, and jailbreaks — and what actually works instead.
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
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
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
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