8 Essential Indexing Strategies to Boost Enterprise RAG Performance

1. Make data easier to split into understandable knowledge chunks

In classic RAG, the LLM only sees a subset of the original data retrieved as knowledge chunks; excessive pronouns, cross‑references, or contradictory semantics within those chunks hinder the model's understanding and generation. An example tutorial segment shows how images and external links are invisible to the LLM.

Recommendation: enrich the chunk with missing details, embed referenced content or a concise summary, so each chunk can stand alone as much as possible.

2. Handle abbreviations, acronyms, and proper nouns

Domain‑specific short forms such as "BD", "PR", or industry‑specific jargon can confuse the LLM without sufficient context.

During indexing, supplement or repair the original text using a translation table or explicit explanations.

Alternatively, feed a separate term‑definition context to the LLM at retrieval time, filtering out irrelevant entries if the context becomes too large.

This approach is especially important for Text‑to‑SQL scenarios where column names may be cryptic.

3. Special handling of multimodal documents

Multimodal files (PDFs with images, tables, charts) require conversion to textual representations before vector storage.

Extract plain text, tables, and images separately using tools such as Unstructured, OmniParse, or LlamaParse.

Use LLMs to generate descriptions and summaries for tables.

Apply OCR for pure‑text images and multimodal vision models (e.g., qwen‑vl, GPT‑4V) to understand complex graphics, then store the generated captions for indexing.

These methods make multimodal knowledge searchable and usable by the LLM.

4. Enrich knowledge semantics

After cleaning, the knowledge must be split into chunks. Chunk size and splitting algorithm affect retrieval precision, context completeness, latency, and token cost.

Guidelines:

Test different chunk_size values and evaluate quality vs. performance.

Use sliding‑window parsers (e.g., SentenceWindowNodeParser) that attach surrounding chunks as metadata.

Combine multiple chunk sizes with hierarchical parsers (e.g., HierarchicalNodeParser) to obtain both fine‑grained retrieval and richer context.

5. Generate additional semantic data for chunks

Beyond the raw chunk, create extra indexed content to improve recall:

Custom data : manually crafted auxiliary information per chunk.

Hypothetical questions : LLM‑generated queries that anticipate user intent.

Similar questions : paraphrased variations of existing QA pairs.

Summaries : concise abstracts for long chunks.

Vectorize either the combined original + generated data or the generated data alone, depending on the use case.

6. Set and leverage metadata

Vector databases allow attaching metadata (document name, timestamp, version, hash, etc.) to each vector. Metadata can be used for:

Maintenance and version‑controlled updates.

Post‑retrieval sorting.

Pre‑filtering before similarity search, dramatically improving speed and relevance.

Example: a legal knowledge base can store law type, region, and date as metadata, then filter to a specific jurisdiction before performing vector similarity.

7. Try different index types

Besides the dominant vector index, consider complementary structures:

Keyword index : extract keywords from each chunk (via LLM or RAKE) and map them to chunks; useful as a lightweight, non‑semantic fallback.

Knowledge‑graph index : represent entities and relationships in a graph database (e.g., Neo4j). This enables precise reasoning over structured connections and can be built from both structured tables and unstructured text using LLM‑driven extraction.

8. Test and choose appropriate embedding model

Embedding models turn text into fixed‑size vectors for similarity matching. Selection criteria include architecture, training data, dimensionality, resource requirements, and language support.

Use benchmarks such as HuggingFace’s MTEB to compare models, and consider fine‑tuning on domain‑specific corpora (e.g., legal or financial texts) to boost retrieval quality.

These eight recommendations form a comprehensive guide for optimizing the indexing stage of production‑ready RAG systems; the next article will address query and generation stages.