RAG changes what a model can see; fine-tuning changes how it behaves. This guide explains when each fits, what data each needs and how to decide with an evaluation set rather than a demo.
RAG or fine-tuning: which one does your product need? Read More »
How to test an LLM feature before launch: build a golden dataset from real inputs, combine automated checks with human review, and block any prompt or model change that fails the regression set.
Yes, you can build a working GenAI MVP in 4 to 8 weeks, as long as it does one job for one type of user and you decide up front how you’ll judge its output. At Dignep it’s a small-squad build, scoped and quoted before work starts. Most overruns come from scope creep and a
Building a GenAI MVP in 4–8 weeks: scope, team and cost Read More »
Getting an AI agent from proof of concept to production usually takes three stages: a PoC that proves the model can do the task on your real data, an MVP that runs it for a limited set of users with logging and guardrails, and production hardening for reliability, permissions and cost. The model is rarely
Vibe coding means describing what you want in plain language and letting an AI tool write the code, judging the result mostly by whether it runs rather than by reading every line. It’s fast and genuinely useful for prototypes and throwaway tools. For software other people depend on, it needs the same review, tests and
Claude Code is Anthropic’s agentic coding tool: it reads your codebase, edits files, runs commands and works with git, from the terminal, an IDE extension, a desktop app or the browser. Used well across the software lifecycle, it takes on the mechanical work (tests, refactors, upgrades, first drafts) while engineers keep ownership of design, review
Using Claude Code across the SDLC: an enterprise guide Read More »
Implementing GenAI in an enterprise works best when you start with one narrow, measurable use case, connect a hosted model to your own data through retrieval, and build an evaluation suite before you build the interface. Most failed projects skipped one of those three. They chose a vague goal, tried to fine-tune too early, or
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