5 Lessons Learned Building RAG Systems

Retrieval augmented generation (RAG) is one of 2025’s hot topics in the AI landscape. These systems combine relevant knowledge retrieval with large language models (LLMs) to enable more accurate, up-to-date, and verifiable responses to user queries (prompts) by grounding generated outputs in external knowledge sources instead of relying solely on information learned from text data during LLM training. However, building production-ready RAG systems requires careful considerations and poses challenges of their own.

This article lists five key lessons learned and commonly discussed across the AI developers’ community from building RAG systems.

1. Quality Trumps Quantity in Information Retrieval

Early RAG implementations primarily focused on quantity over quality in the retrieval stage, meaning they aimed to retrieve large volumes of content matching the user query. However, experimental research showed that retrieval quality matters significantly more than quantity, with RAG systems that retrieve fewer but more relevant documents outperforming in most cases those that try to retrieve as much context as possible, resulting in an overabundance of information, much of which might not be sufficiently relevant. Quality in retrieval requires investing efforts in building effective text embedding models and advanced relevance-based ranking algorithms to decide what to retrieve. Evaluating retrieval performance using metrics like precision, recall, and F1-score can further help in refining retrieval quality.

TL;DR → Quality over quantity: Prioritize retrieving fewer but highly relevant documents to enhance output accuracy.

2. Context Window Length is Critical

Effective management of context windows in RAG systems, that is, the limited amount of text an LLM can process at once during generation, is essential in building stellar-performing RAG systems. Since LLMs on the generator side of the system tend to focus more on the initial and final parts of the context, a simple concatenation of retrieved documents might lead to suboptimal results where key information is partly missed: this problem is known as position bias and context dilution. Modern strategies like hierarchical retrieval and dynamic context compression help optimize the way retrieved information is turned into a context passed to the LLM. For example, case studies have demonstrated notable improvements in response accuracy when these techniques are applied.

TL;DR → Manage context windows carefully: Optimal context handling prevents key information loss and improves system performance.

3. Reducing Hallucinations Requires Systematic Verification

RAG systems partly exist for the sake of reducing common hallucinations in standalone LLM, but the problem is not completely eliminated. Experience in building RAG systems has shown that the most effective and hallucination-proof systems need built-in verification schemes, like self-confidence checking and confidence scoring, for cross-checking generated outputs against information retrieved earlier on in the pipeline, thereby maintaining factual accuracy. Incorporating these verification methods systematically can significantly curb the issue of hallucinations.

TL;DR → Systematic verification is key: Integrate robust checking methods to significantly reduce hallucinations in generated responses.

4. Retrieval Computation Costs Exceed Generation Costs

Contrary to what one may think, the computational overhead of state-of-the-art retrieval schemes has frequently necessitated more time cost than the text generation process itself. This is particularly true for hybrid retrieval techniques that combine keyword and semantic search. A careful architecting of the retrieval infrastructure with caching and index optimization solutions is key to making retrieval solutions in RAG systems more efficient. Engineers should consider benchmarking both retrieval and generation components separately to optimize overall system performance.

TL;DR → Optimize retrieval costs: Streamline your retrieval pipeline since it often requires more computation than generation.

5. Knowledge Management is a Continuous Process

As the retrieval document corpus grows, RAG systems require continuous knowledge management. Organizations have seen how successful RAG systems in production require systematic approaches for content refreshing, managing conflicts or contradictions among stored documents, and knowledge validation. Therefore, successful production RAG systems demand dedicated knowledge
engineering assets and governance processes to be put in place. Regular monitoring and updating of stored content is essential to ensure ongoing relevance and accuracy.

TL;DR → Continuously manage knowledge: Regular updates and validation of stored content are essential for maintaining system relevance.

Wrapping Up

In summary, building RAG systems requires a careful balance of high-quality retrieval, strategic context management, and robust verification to ensure accurate outputs. Engineers must continuously refine their techniques, addressing challenges like computational overhead and context dilution while preventing hallucinations through systematic validation. The key takeaway is to prioritize quality and rigorous performance benchmarking as the foundation for innovation in AI-driven retrieval and generation.