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Lessons from implementing semantic search for an enterprise financial platform.
Enterprise financial platforms accumulate tribal knowledge at scale. Experienced practitioners hold critical understanding of platform constraints, query patterns, and troubleshooting approaches — knowledge that takes months to transfer through traditional onboarding. New contractors face 3-4 week ramp-up periods. Senior resources spend 40-50% of their time answering repetitive technical queries.
The hypothesis: a Retrieval-Augmented Generation system could capture institutional knowledge in a vector database, enabling semantic search and AI-synthesized responses. The implementation revealed several domain-specific challenges not apparent in standard RAG tutorials.
The system follows a standard RAG pattern with modifications for financial domain content:
Documents flow through ingestion (load → chunk → embed → store), then queries trigger retrieval (embed query → similarity search → context injection → LLM synthesis).
Standard chunking approaches fragment financial content in problematic ways. A 500-token chunk mid-calculation loses context about what the calculation achieves. Code examples split across chunks become syntactically incomplete.
The issue: Financial platform documentation contains interconnected concepts — FX rates reference currency pairs, consolidation logic references entity hierarchies, query patterns reference dimensional constraints. Naive chunking severs these relationships.
Mitigation attempted:
Chunking remains an active challenge. The optimal strategy appears content-type dependent — different parameters for code patterns versus conceptual explanations versus troubleshooting guides.
Financial platforms develop internal vocabulary. "Intersection" means something specific. "Slices" have platform-defined semantics. "Elimination entries" reference consolidation concepts not present in general embeddings.
The issue: Embedding models trained on general corpora lack domain-specific semantic proximity. A query about "elimination entries" may not retrieve documents about "intercompany adjustments" despite functional equivalence.
Observations:
Fine-tuned embeddings would address this systematically but require substantial training data and compute budget — outside POC scope.
Many enterprise platforms implement proprietary query languages. These share structural similarity with SQL but carry platform-specific semantics, constraints, and syntax requirements.
The issue: Embedding a code block captures syntax but loses constraint knowledge. The system retrieves similar-looking queries without understanding platform-specific rules — no aliasing permitted, explicit column specification required, character limits enforced.
Approaches tested:
Code generation remains higher-risk than code explanation. The system performs better at interpreting existing queries than generating compliant new ones.
RAG system quality depends fundamentally on knowledge base quality. This dependency was intellectually understood but practically underestimated.
The issue: Curating 10-15 seed documents required significantly more time than technical implementation. Historical issue resolutions existed in tickets, Slack threads, and undocumented team memory — not in embeddable markdown.
Content gaps identified:
The POC validated retrieval mechanics but exposed that production deployment requires sustained content curation effort — a people problem, not a technology problem.
Financial services domains carry higher accuracy requirements than typical RAG applications. An incorrect consolidation explanation or flawed FX conversion pattern creates real business risk.
The issue: LLM hallucination — even at low temperature settings — produces plausible-sounding but incorrect financial calculations. Users may trust AI-generated content that contains subtle errors.
Mitigations implemented:
Complete hallucination prevention remains impossible. The system design assumes human review of AI-generated content before production use.
Production deployment introduces constraints absent in local POC development:
Production architecture likely requires self-hosted embeddings and potentially on-premises LLM deployment — significantly increasing infrastructure complexity.
The POC validated core RAG mechanics for financial domain content. Retrieval relevance reached approximately 75% on test queries — below the 80% target but demonstrating feasibility. Answer accuracy rated "helpful" on 70% of evaluated responses.
Key learnings:
For teams considering RAG implementations in regulated industries:
The technology works. The challenge is institutional: capturing knowledge that exists only in people's heads and maintaining it as systems evolve.
Technical Stack: Python, LangChain, ChromaDB, OpenAI GPT-4o, OpenAI text-embedding-3-small, Streamlit, FastAPI
Domain: Enterprise financial platforms, consolidation systems, proprietary query languages