What Is RAG?

What Is RAG?

RAG (retrieval-augmented generation) is a RAG-family architecture that retrieves external context before an LLM generates an answer, then asks the model to ground its response in that context. It shows up in production as retriever spans, retrieved chunks, context passed to the model, and final answer traces. FutureAGI evaluates RAG with surfaces such as RAGScore, ContextRelevance, and Groundedness, so teams can tell whether a failure came from retrieval, generation, or unsupported claims.

Why RAG Matters in Production LLM and Agent Systems

RAG fails quietly when the retriever fetches weak evidence and the generator writes fluent prose anyway. The user sees a confident answer. The trace may show a normal latency profile. The business impact appears later as wrong policy guidance, support escalations, bad citations, or an agent taking an action based on a stale document.

The pain splits across teams. Application engineers need to know whether to fix chunking, embeddings, reranking, prompt instructions, or the model. SREs watch p99 latency, token-cost-per-trace, retrieval timeout rate, and sudden changes in the number of retrieved chunks per request. Product and compliance teams care about the downstream symptom: answers that cite irrelevant documents, omit the source of a claim, or blend two policies that should never be mixed.

In 2026 multi-step pipelines, RAG is rarely a single search call before a chat response. Agentic systems retrieve context for planning, tool selection, customer policy checks, code execution, and human handoff summaries. One retrieval miss can become a wrong tool call several steps later. Unlike a standalone Ragas faithfulness check, production RAG debugging needs component signals that separate retrieval quality from generation grounding and final answer usefulness.

How FutureAGI Handles RAG

FutureAGI’s approach is to treat RAG as an evaluable trace, not just a prompt pattern. The anchor surface is the RAGScore evaluator from fi.evals: it combines retrieval relevance, answer grounding, and response quality into one production score. Engineers can pair it with ContextRelevance, Groundedness, and ChunkAttribution when they need to isolate the failing layer.

Consider an internal support assistant built with LangChain and a vector database. The application instruments the pipeline with traceAI-langchain, so each request records the user input, retriever span, retrieved chunks, generator call, model output, token usage, and latency. FutureAGI samples those traces into an evaluation dataset and runs RAGScore on every candidate answer. If the score drops after a knowledge-base migration, the team opens the trace cohort and checks the component metrics.

A low ContextRelevance score points to search: chunk size, embedding model, top-k, filters, or reranker settings. A low Groundedness score with strong context points to generation: prompt instructions, citation formatting, or model choice. A missing ChunkAttribution signal means the answer cannot be tied back to a retrieved source. The engineer then sets a metric threshold, creates a regression eval from the failing traces, and routes high-risk cases to a fallback answer or human review through Agent Command Center.

How to Measure or Detect RAG Quality

Measure RAG at retrieval, generation, and answer layers:

• RAGScore returns a combined RAG quality score for a query, retrieved context, and generated answer.
• ContextRelevance detects whether retrieved chunks answer the user’s query before generation happens.
• Groundedness checks whether the final answer is supported by the provided context.
• Trace signals include retriever latency, retrieved chunk count, token-cost-per-trace, citation-missing rate, and eval-fail-rate-by-cohort.
• User proxies include thumbs-down rate on sourced answers, escalation rate after knowledge-base answers, and citation click-through.

Also separate retrieval absence from poor retrieval. If no documents are returned, alert on empty-context rate; if documents are returned but irrelevant, alert on low ContextRelevance. This prevents the dashboard mistake of mixing outages, ranking regressions, and generation hallucinations into one quality metric.

from fi.evals import RAGScore

score = RAGScore().evaluate(
    input="What is our refund policy?",
    output=answer,
    context=retrieved_chunks,
)
print(score.score, score.reason)

The important detection pattern is not one global score. Track RAGScore by dataset, retriever version, document collection, model, and prompt version so a release can fail only the cohort it actually changed.

Common Mistakes

RAG problems usually come from treating the whole pipeline as one model call:

• Scoring only the final answer. A single score hides whether retrieval, context packing, or generation failed.
• Optimizing top-k without relevance labels. More chunks can increase distractors and cost while lowering Groundedness.
• Running evals without storing retrieved chunks. The evaluator needs context; answer-only traces cannot explain the source of a failure.
• Treating stale content as hallucination. The model may be grounded in an outdated document. Version the corpus and trace document timestamps.
• Skipping regression evals after reindexing. Embedding, chunking, and metadata-filter changes can alter answers even when prompts stay fixed.

A useful review question is simple: can you name the exact retrieved chunk that made the answer pass or fail?