A practical guide to retrieval-augmented generation strategies

But RAG isn’t a one-size-fits-all approach. From simple retrieval to agent-driven orchestration, there are multiple strategies that vary in complexity, performance and suitability for different business scenarios. Understanding these strategies (and when to apply them) is key to building AI systems that actually deliver value.

Basic retrieve

At its core, RAG begins with simple retrieval. This strategy pulls the most relevant documents from a knowledge base typically using vector embeddings and similarity search then includes them directly in the prompt to the LLM. It’s a fast and lightweight setup often used to build initial prototypes or deploy internal tools. For example, a customer support assistant might use this method to retrieve relevant FAQs or help articles in response to user queries.

The simplicity of basic retrieve is also its limitation. It assumes that the top-ranked documents are always the most relevant which isn’t always the case. If the retrieval isn’t precise or the context window is too limited the model may misinterpret or miss key information. It works best when you have a clean, focused corpus and low tolerance for latency but it can struggle with nuance or ambiguity.

Retrieve and re-rank

A step up from basic retrieval is the “retrieve and rerank” strategy. Here, the system fetches a wider set of candidate documents and then uses a secondary model to re-evaluate and reorder them by relevance before presenting them to the LLM. This method improves the quality of context passed to the model, reducing noise and increasing answer precision. In the financial services sector, for example, a compliance assistant could use this technique to extract and prioritise the most relevant sections from lengthy regulations.

The trade-off is latency and complexity. Reranking introduces an extra step in the pipeline and may require custom tuning to balance performance and speed. It also increases infrastructure demands and may still fall short in domains where context is dispersed across multiple documents.

Graph-enhanced retrieval

For domains with complex relationships between entities, concepts or datasets, graph-enhanced retrieval brings a layer of structure to the search process. Knowledge graphs can help navigate from one data point to another enabling systems to retrieve not just documents but interconnected insights. This is particularly powerful in sectors like pharmaceuticals, where researchers need to link symptoms, treatments and trial results across multiple sources.

However, graph-based RAG strategies require significant upfront effort. Building and maintaining a high-quality knowledge graph involves manual curation or sophisticated entity extraction pipelines. It also assumes that the domain knowledge is stable enough to be modeled explicitly. When done well, this strategy boosts relevance and explainability but it’s not ideal for fast-moving or loosely structured domains.

Multimodal retrieval

Not all knowledge lives in documents. Multimodal retrieval expands the scope of RAG to include images, audio, video and scanned files bringing AI into workflows where insight comes from multiple formats. For instance, a field service platform could use this approach to let engineers upload a photo of equipment, retrieve relevant diagrams or instructions and get contextual AI guidance.

This added capability introduces new challenges. Extracting meaningful content from non-text formats relies on upstream processes like OCR, transcription, and image embedding which can be error-prone or require specialised models. Multimodal RAG can dramatically expand what AI can “see” and respond to but it demands a more robust pipeline and careful evaluation of input quality.

Agentic RAG

The most advanced strategy in this landscape is agentic RAG. Rather than simply answering questions with retrieved content, the LLM acts as a reasoning agent using tools, APIs, memory and logic to perform multi-step tasks. These systems can plan, retrieve, iterate and even trigger actions. One compelling use case is in strategic reporting, where an AI agent gathers internal data, queries financial systems and assembles a custom summary for leadership.

But agentic systems come with real complexity. They need orchestration logic, execution monitoring and strong safeguards against failure. Without tight control, a misstep in one task can compound downstream. This approach also raises questions of traceability and trust which are critical in regulated industries.

Where to begin

Choosing the right RAG strategy isn’t just a technical decision - it’s a business one. The best starting point often depends on your existing data, risk appetite and speed to value. That’s why we offer a focused, low-risk path through our AI PoC Accelerator, helping organisations quickly test which RAG approach fits their problem space.

Whether you're looking to improve knowledge access, automate decision support, or create intelligent workflows, retrieval-augmented generation opens the door to meaningful, scalable AI. The key is to start with purpose, test with discipline and scale with confidence.