Engineering Blog — Page 4
Practical notes on AI systems, product engineering, architecture, and shipping reliable software.
From AI Demo to Production Product: The Improvement Roadmap Teams Skip
The stages after a shiny demo: reliability, evals, UX for uncertainty, cost control, security, and ops—so your AI feature becomes a product customers keep.
AI SaaS Pricing Models That Work: Seats, Usage, Outcomes, and Hybrid Plans
How to price AI products without lighting money on fire—token pass-through traps, seat vs usage, outcome pricing, free tiers, and enterprise packaging.
How to Validate an AI Product Idea Before You Write Code
A founder-friendly validation playbook for AI products: problem interviews, fake-door tests, wizard-of-oz MVPs, willingness to pay, and kill criteria.
25 AI Product Business Ideas That Can Actually Become Companies (2026)
A practical shortlist of AI product ideas with real buyers, defensible wedges, and build notes—not generic “ChatGPT wrappers” that die in a month.
How to Evaluate and Version Prompts: From Guesswork to a Prompt Library
Build a professional prompt workflow: golden datasets, offline evals, A/B tests, semantic versioning, and ownership so AI features improve instead of randomly drifting.
Prompt Injection and AI Security: How to Protect LLM Apps and Agents
Understand direct and indirect prompt injection, data exfiltration via tools, and practical defenses for chatbots, RAG, and agents in software products.
RAG and Prompt Engineering: Grounding AI Answers in Your Company Knowledge
How retrieval-augmented generation works with prompts: chunking, citations, refusal behavior, and why “just fine-tune” is often the wrong first step.
Prompt Engineering for Business: Marketing, Sales, Support, and Operations
Ready-to-adapt prompt playbooks for GTM teams—campaign briefs, sales emails, support macros, meeting notes, and SOPs without brand or compliance disasters.
System Prompts, Temperature, and Model Settings: Controlling LLM Behavior
How system vs user messages, temperature, top-p, max tokens, and stop sequences change model behavior—and recommended defaults for product features.
How to Use Prompt Engineering for Software Development and Code Reviews
Prompt patterns for coding, debugging, refactors, tests, and PR review—plus policies so AI help does not invent APIs or ship insecure code.