Insights
How we think about AI engineering.
Original writing from the AIEvolveYes team on LLMs, RAG, agents, voice AI and what it actually takes to get AI systems into production.
Fine-Tuning LLMs for Business-Specific Applications
Fine-tuning can make a model genuinely fluent in your business — or waste weeks solving a problem prompting already handled. Here's how to know which situation you're in.
Building Reliable RAG Systems for Private Knowledge
Retrieval-augmented generation sounds simple — connect a model to your documents — but most RAG systems that ship are unreliable in ways that only surface once real users start asking real questions.
How to Build Reliable AI Agents
The hard part of agent systems was never getting a model to reason well — it's containing a mistake before it compounds across a dozen sequential steps.
Voice AI Assistants for Business Automation
Voice is often the fastest way to communicate a request — but only if the assistant on the other end can handle a real request, not a fixed list of trigger phrases.
RAG vs Fine-Tuning: Choosing the Right Approach
Both get pitched as 'customize the model,' which is exactly why teams pick the wrong one. They solve two fundamentally different problems.
Designing Production-Ready LLM Applications
A demo that impresses in a meeting can still be unusable in production if it has no error handling, no cost controls and no way to detect quality drift.
How AI Agents Can Automate Business Workflows
Rule-based automation handles the mechanical parts of a workflow. It's everywhere judgment and unstructured input show up that it breaks down — which is most of what makes a workflow hard to automate in the first place.
Computer Vision for Real-World Business Applications
A model trained and tested only on clean, well-lit sample images tells you almost nothing about how it will perform on what a phone camera actually captures.
Building Multimodal AI Systems
Running two single-input models and concatenating their outputs isn't multimodal fusion — and it doesn't capture the context that makes combining modalities worthwhile in the first place.