Ai Engineering Articles | Auroviq Blog
Practical notes on AI systems, product engineering, architecture, and shipping reliable software.
Modern AI Dev Stack 2026: LangChain, LlamaIndex, vLLM, Vector DBs—What to Use and Why
A clear map of the AI application stack: orchestration libs, serving, embeddings, evals—what’s new, when to use each layer, and what to skip early.
Meta’s Open-Source AI Strategy: How Llama Changes the Build Calculus for Startups
Why Meta open-sources frontier-class models, when self-hosting Llama makes sense, and when API models still win for product teams.
Apple Intelligence and On-Device AI: Privacy Marketing or a Real Product Shift?
What Apple’s on-device and Private Cloud Compute approach means for app builders, data strategy, and competing with cloud-first AI products.
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.
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.
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.
Advanced Prompt Engineering Techniques: Chain of Thought, Roles, and Structured Outputs
Go beyond basic prompts: chain-of-thought, self-critique, tool-aware prompting, JSON schemas, and multi-step workflows that raise accuracy for complex tasks.
Prompt Engineering Explained: How to Write AI Prompts That Actually Work
A practical introduction to prompt engineering: anatomy of a strong prompt, role–task–context–format, common failures, and a checklist for product teams.
AI-Assisted Coding Best Practices for Professional Teams (Without Shipping Slop)
How engineering teams use coding agents and LLMs safely: review standards, secret hygiene, architecture constraints, and productivity metrics that are not vanity.