What retrieval strategy fits the corpus: dense embeddings, lexical BM25, hybrid re-ranking, or knowledge graph traversal?
Architectural Pillar
RAG & Knowledge Systems
Designing enterprise retrieval-augmented generation (RAG), verified source attribution, hybrid search, and retrieval evaluation.
Problem Territory
Engineering Problem Territory
Problem class: Retrieval architecture, knowledge provenance, and verification
Problem Territory
Pairing a vector database with an off-the-shelf embedding model offers a deceptively easy demo for querying proprietary documentation. In real-world environments, however, so-called “naive RAG” quickly breaks down: fixed-window chunking shears across relevant context, semantic similarity retrieves superficially related noise, structured tabular data is distorted, and the system fails to account for conflicting document revisions.
The RAG & Knowledge Systems pillar encompasses end-to-end knowledge architecture: from domain-aware document parsing and hybrid retrieval (combining lexical BM25, dense embeddings, and cross-encoder re-ranking) to rigorous answer attribution and continuous evaluation of retrieval faithfulness.
Context
When This Expertise Is Relevant
Typical engineering scenarios and system lifecycle stages where architectural engagement creates maximum leverage.
Naive RAG setups (fixed chunking and semantic vector similarity) yield hallucinated answers, omit critical context, or blend conflicting documents.
Architects must decide between dedicated retrieval pipelines, model fine-tuning, or expanding context windows under latency and cost budgets.
Enterprise corpora involve tabular, hierarchical, or relational records that lose structural meaning under conventional embedding models.
Strict provenance, source attribution, and document-level access control lists (ACL) are mandatory for compliance or customer trust.
Architecture Decisions
Key Architectural & Decision Questions
Structural decisions that govern system reliability, component boundaries, and production unit economics.
How should multi-modal documents be parsed, segmented, and enriched to prevent fragmentation of core meaning?
How are vector and lexical search indices kept synchronized with continuous updates and deletions in authoritative data stores?
What automated evaluation harness (context relevance, groundedness, answer attribution) monitors retrieval quality in CI/CD?
Deliverables
Typical Engineering Deliverables & Decisions
Concrete technical artifacts, evaluation benchmarks, and architectural decision records.
End-to-End RAG Architecture Specification covering ingestion, chunking, indexing, retrieval, re-ranking, and response synthesis.
Retrieval Strategy Benchmark Report comparing latency, recall, and factual precision across realistic query distributions.
Corpus Ingestion Standards including metadata schemas, access control inheritance, and hierarchical parsing rules.
Attribution Verification and Hallucination Filtering Protocols for production inference.
Scope Boundaries
What This Work Is Not
Clear scope boundaries preventing commoditized service interpretations and ensuring focused technical leadership.
- Not installing out-of-the-box commercial chat widgets over uncurated file folders.
- Not generic relational database administration or traditional data warehousing.
- Not claiming vector search alone solves complex knowledge representation or domain reasoning.
- Not ignoring latency, inference cost, and stale-data trade-offs in favor of oversized prompts.
Evidence Base
Related Research & AI Lab Studies
Empirical validation and laboratory fault-injection studies verifying architectural patterns.
When an AI Product Does Not Need an LLM: Deterministic Inference, Provenance and Controlled Uncertainty
An engineering decision framework for choosing between generative LLMs and deterministic inference in production runtime. Why replacing an explicit knowledge base with stochastic prompts erodes reproducibility and trust, how to structure claim-level provenance, and which objective criteria determine where a foundation model creates genuine value versus unnecessary cost and liability.
Perfume Engine: An Evidence-Backed Recommendation Prototype Without a Runtime LLM
Perfume Engine began as a broad fragrance-intelligence concept, but was deliberately narrowed during an early iteration into a controlled validation prototype: 12 perfume identities, 6–8 editorial mappings, exact search, manual evidence collection, and deterministic recommendation logic. The experiment was not designed to inject an LLM at every layer, but to verify whether a recommendation system could remain explainable, reproducible, and provenance-first. Because Perfume Engine subsequently evolved into a broader system, this study documents a specific historical architecture experiment rather than the full current product architecture.
Engagement
Discuss System Architecture
If you are designing a complex AI system or encountering production stability bottlenecks, prepare your requirements for asynchronous review.
Initiate a Structured Discussion
Review qualification criteria and advisory formats on the Work With Me page to evaluate architectural fit.