RAG in Practice: Stack, Prompts and Use Cases
Simply retrieving documents in a RAG system might not always provide the full, nuanced context an LLM needs for advanced tasks. The RAG Context Enrichment Stack focuses on adding layers of intelligence to the retrieved information before it reaches the LLM. This guide pulls together everything on Onexial tagged rag — 7 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind RAG
Before wiring anything together, the vocabulary has to be precise. These 2 definitions cover the terms that show up in almost every RAG discussion — each one links to a full entry with an example and its own connections inside the graph.
The RAG tool stack
A stack is a set of tools chosen for one job, not a list of favourites. These 3 stacks show which combinations hold up in production for RAG, and what each layer is actually responsible for.
AI Support Agent Stack
Tier-1 support handled by an AI agent grounded on your docs, with human handoff.
RAG Context Enrichment Stack
A technical stack designed to enrich the contextual data provided to a Retrieval Augmented Generation (RAG) system, improving the quality and depth of LLM responses.
Low-Cost RAG Stack
This stack combines open-source and cost-efficient components to build a Retrieval-Augmented Generation (RAG) system with minimized operational expenses.
Trade-offs and comparisons
Most RAG decisions are trade-offs rather than right answers. These 1 comparisons break down the real differences, when each option wins, and the recommendation for the common case.
Prompts you can reuse
Prompts are reusable components. Each of these 1 prompts is written to be dropped into a RAG workflow with minimal editing, including the context it expects and an example output.
Frequently asked questions
- How does context enrichment help with complex queries?
- For complex queries, enrichment can break down the query into sub-questions, retrieve diverse types of information, and then synthesize them. For example, it might identify entities in the query, search for those entities in a knowledge graph, and add relevant attributes to the context, providing a richer understanding to the LLM.
- Is this stack suitable for real-time applications?
- Yes, but careful optimization of each component is required to minimize latency. Techniques like caching, efficient API calls, and optimized data processing pipelines are crucial for maintaining real-time performance while performing complex enrichment steps.
- How do open-source embedding models save costs in RAG?
- Open-source embedding models (like those from Hugging Face) save costs by allowing you to run them locally or on your own low-cost compute, avoiding per-call API charges from commercial providers. While they may require managing infrastructure, the operational cost per embedding is often significantly lower for high-volume use cases compared to proprietary alternatives.
- What is the primary benefit of using LiteLLM/OpenRouter in a low-cost RAG stack?
- The primary benefit of LiteLLM/OpenRouter is dynamic model routing. It allows the RAG system to intelligently send queries to the cheapest LLM that can adequately handle the task, switching between various open-source or commercial models based on current pricing and performance. This avoids being locked into a single expensive model and optimizes cost for every single LLM call within the RAG pipeline.