TECHNIQUE
Data & Context Engineering
Document ETL for LLMs is deployed as source-specific ingestion, normalization/chunking, hybrid retrieval, ranking/post-processing, and workflow controls over private work content.
Build connectors or loaders for proprietary work sources instead of relying on general-model memory.
5 of 6 operators in the roster; Grab is counted from deployed td_102, not the announced teardown.Normalize or enrich incoming documents into LLM-friendly representations such as markdown tables, generated documentation, or extracted text before downstream use.
3 of 6 operators in the roster; Grab is counted from deployed td_102 only.Chunk content and generate embeddings for semantic retrieval or LLM context construction.
4 of 6 operators in the roster.Use hybrid retrieval: lexical/BM25 or traditional work signals alongside neural semantic retrieval.
4 of 6 operators in the roster.Rerank, de-duplicate, personalize, ACL-filter, or otherwise post-process retrieved context before answer generation or display.
4 of 6 operators in the roster.Precompute and store document artifacts such as chunks, embeddings, titles, summaries, FAQs, graph representations, or indexed catalog entries for later retrieval.
4 of 6 operators in the roster; Grab is counted from deployed td_102 only.Add query understanding, source selection, multi-step query expansion, or agent routing around retrieval and document-processing workflows.
4 of 6 operators in the roster.Put human review, HITL, or security-control layers around critical document outputs or agent execution.
3 of 6 operators in the roster.Embed document-LLM results into existing user surfaces such as search, Slack, file preview, ERP handoff, or workflow applications.
5 of 6 operators in the roster; Grab is counted from deployed td_102 only.Across the observed deployed/pilot operators, the technique is applied to private, work, or business-specific content: work artifacts, third-party work apps, data catalogs, real-estate workflow documents, internal/private documents, internal knowledge sources, or invoices.
Retrieval stack choice differs by use case.
APPROACH 01
Hybrid retrieval with lexical/BM25/traditional signals plus neural semantic retrieval.
APPROACH 02
Enterprise-search-hosted catalog access with an LLM app and custom prompt over indexed datasets.
APPROACH 03
Workflow document extraction and quality-control agents rather than a retrieval-index-first product surface.
Chunking and indexing timing is not uniform.
APPROACH 01
Pre-enrich, chunk, embed, index, and store artifacts before query-time retrieval.
APPROACH 02
Chunk documents on the fly at query time to pull only relevant sections.
Model strategy ranges from model-agnostic/product LLMs to fine-tuned task or retrieval models.
APPROACH 01
Keep the RAG system model-agnostic or use an LLM app over an existing enterprise search platform.
APPROACH 02
Fine-tune or evaluate specialized models for retrieval quality or document extraction.
Non-text, multilingual, low-quality, or highly varied document formats require extra processing such as OCR, image augmentation, transcription, or multimodal understanding.
Naive single-prompt or insufficiently controlled agents can fail on complex multi-step workflows, producing false positives, false negatives, or wrong paths.
Answer and retrieval quality remain operational concerns; operators report accuracy/relevance challenges and use metrics, reranking, or LLM judging to manage them.
Private and sensitive corpora require grounding, citations, ACLs, or access-control-aware ranking so systems do not misinform users or surface the wrong content.
| Name | Kind | When | Maturity |
|---|---|---|---|
| Unstructured.io | library | broad-format document parsing into LLM-ready elements | established |
| Docling | library | high-fidelity PDF layout, tables, and reading order matter | emerging |
| Managed OCR (Textract / Document AI) | service | scanned and image-heavy documents at volume | established |