— INSIGHTS
How this work actually gets done
Long-form technical writing on the decisions that determine whether an AI system survives contact with production — architecture, evaluation, cost, and choosing who builds it.
- 9 min readPardhasaradhi Menda
How RAG Pipelines Work for Enterprise Search
Retrieval-augmented generation (RAG) is a technique that answers a question by first retrieving relevant passages from your own documents, then asking a language model to answer using only those passages — so the answer is grounded in your content and can cite where it came from.
- RAG
- Enterprise Search
- Vector Databases
- LLM Architecture
- 9 min readPardhasaradhi Menda
LLM Fine-Tuning vs. RAG: When to Use Each
Fine-tuning changes how a model behaves; retrieval-augmented generation changes what a model knows at the moment it answers. Facts that change belong in retrieval, behaviour that must be consistent belongs in fine-tuning, and most production systems need both.
- Fine-Tuning
- RAG
- LLM Architecture
- Model Selection
- 8 min readVarshith Dondamuri
AI Agency Selection Checklist for Enterprises
Choosing an AI development partner comes down to six verifiable things: shipped production systems, a stated evaluation method, explicit data and IP terms, a named team, an honest cost model, and a handover plan that leaves you independent.
- Vendor Selection
- AI Strategy
- Procurement
- Enterprise AI
Have a harder version of one of these problems?
These articles describe the general case. Yours will have a constraint that changes the answer — that is the conversation worth having.