New post in our blog! 🤖 Building better AI agents? Explore how RAG, MCP, and Ollama work together
agents llama rag
| Source: Mastodon | Original article
Codeminer42’s latest blog post, “Building a Practical AI Agent with RAG, MCP and Ollama,” walks developers through a concrete recipe for stitching together Retrieval‑Augmented Generation, Model‑Contextual Prompting and the open‑source Ollama runtime. The three‑step guide shows how to pull external knowledge into prompts, shape the model’s reasoning with MCP and run the whole stack locally on Ollama, producing agents that are both more factually grounded and less dependent on costly cloud APIs.
The timing is significant. As we reported on March 30, the Reflective journaling companion demonstrated how MCP can tighten the feedback loop between a user’s context and Claude’s output. Codeminer42 now extends that insight to a broader class of agents, answering a growing demand for solutions that combine the factual safety of RAG with the flexibility of prompt‑level control, all without surrendering data to third‑party services. For Nordic firms that prioritize data sovereignty and lean operational budgets, the ability to host LLMs on‑premise via Ollama could lower barriers to deploying AI assistants in customer support, internal knowledge bases or compliance monitoring.
The post also dovetails with the recent Claw‑Eval benchmark, which highlighted the competitive edge of open‑source agents that can efficiently retrieve and reason over external information. By publishing a step‑by‑step implementation, Codeminer42 not only validates the benchmark’s findings but also provides a template that could accelerate the next wave of enterprise‑grade agents.
Watch for follow‑up releases from Codeminer42 that may benchmark their stack against emerging standards, and for announcements from Ollama about performance upgrades or integration hooks. The broader AI community will be keen to see whether this practical recipe translates into measurable gains in reliability and cost‑effectiveness across the Nordic AI ecosystem.
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