Optimize Your Search Relevance

Result quality depends on you

xAI’s search engine is powerful, but it only works with the material you give it. A poorly prepared collection yields mediocre results even with the best model, leading teams to wrongly conclude “RAG doesn’t work.” You control three levers: document structure, query formulation, and collection organization. This lesson examines them in that order, then offers a method to measure your progress.

Prepare documents for chunking

The engine relies on document structure to split content into chunks: titles, subtitles, and paragraphs serve as markers. A heading like “Refund Policy for Online Purchases” gives the chunk a clear identity and makes it findable; “Section 3.2.1” provides no usable signal for chunking or semantic matching. Renaming headings in an internal manual before indexing is often the highest-ROI optimization in the entire project.

Document size matters next. A 500-page PDF produces hundreds of chunks that compete against each other: the larger and more varied a document’s internal corpus, the higher the risk that an irrelevant excerpt scores well. Split these monoliths into thematic sections, aiming for documents of twenty to fifty pages dedicated to a single subject rather than an omnibus document covering everything.

Then there’s the question of context. Each chunk is read in isolation, torn from what precedes it, which dooms anaphoric references. A sentence like “as mentioned above, this deadline also applies to bulk orders” becomes unusable once chunked: neither the deadline nor its subject appears in the fragment. Write “the fourteen-day withdrawal period also applies to bulk orders” and the chunk stands on its own.

Write better queries

Query precision weighs as much as corpus quality. A search for “security” returns dozens of disparate excerpts, from passwords to physical access control; “firewall configuration for incoming connections on port 443” immediately targets the two or three useful passages. When your documents use established technical vocabulary, reuse it: even in hybrid mode, which mixes exact matching and semantic understanding, the literal term remains a strong signal.

Prefer full questions over keyword packets. “API connection timeout” leaves the engine guessing your intent, whereas “How do I configure the API connection timeout?” provides a grammatical structure to match against indexed sentences. The gain is particularly clear in semantic mode, which was designed for natural language.

Leverage metadata and organization

If you declared metadata with inject_into_chunk, it is incorporated into the indexed text and becomes anchor points. A department field injected into every chunk means the query “validation procedure in the marketing team” spontaneously surfaces marketing department documents, without any explicit filter set.

Thematic collection organization produces the same effect at a higher scale. Rather than a single collection containing everything, separate col_docs_techniques for technical documentation, col_procedures_internes for processes, and col_support_client for FAQs and user guides. A customer-facing assistant then queries only the third: fewer documents to search means more precise results, and a lower cost since no calls go to useless collections. Search mode completes this setting—keyword for code or exact reference, semantic for a conceptual question, hybrid when in doubt and by default.

Measure before deploying

None of this matters without evaluation. Before production, build a test set: formulate ten to twenty questions representing real cases, the ones your users actually ask, not the ones you imagine. Run them, judge the relevance of the returned results, and note queries that come back with inadequate excerpts. These tell you what to fix: improve the relevant document’s structure, add metadata, or rephrase the query itself.

Scores serve as your thermometer during this loop. Each result carries a value between 0 and 1; if your typical queries consistently cap below 0.5, don’t look for the problem on the model side. Either your documents are poorly structured, your questions remain too vague, or—most frequently and most misdiagnosed—your corpus simply contains nothing on the requested subject. Re-running this same question set after each modification tells you, with figures, whether you are progressing.

Key takeaways

• Result quality depends on document structure, query precision, and collection organization
• Explicit titles and reasonable document sizes improve chunking
• Full natural language questions yield better results than isolated keywords
• inject_into_chunk metadata enriches chunks and improves relevance
• Test your collections with typical queries before production deployment