Field Notes
Our Blog
Insights on AI, Machine Learning, Web Development, and emerging technologies from industry experts.
// jump to
PristrenPristrenInsights on AI, Machine Learning, Web Development, and emerging technologies from industry experts.
// jump to
49–60 of 528
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
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
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
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 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 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
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
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
ReAct prompting alternates Thought, Action, and Observation steps so the model commits reasoning before choosing an action. Here is how to use it effectively in 2026.
Mahmudul Haque Qudrati
CEO & ML Engineer
Constitutional AI gives models a set of principles to evaluate their own outputs. You can apply the same pattern in your prompts to improve quality.
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
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