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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Maximize output quality by applying structured reasoning pathways and agentic planning frames directly inside prompts.
Mahmudul Haque Qudrati
CEO & ML Engineer
How to guarantee LLMs conform to schema specifications for databases and APIs using instructor libraries and native compiler features.
Mahmudul Haque Qudrati
CEO & ML Engineer
Treating prompts as code: how to track prompt changes, version them in git, and run automated regression tests on code changes.
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
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
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
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
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
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
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
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