Expertise

RAG and Knowledge Retrieval

Building retrieval systems that connect language models with documents, CMS content, metadata and vector search.

Problems I solve: Making private or structured content searchable by AI, Improving answer grounding, Connecting CMS content to a reasoning layer, Designing indexing and reindexing workflows, Evaluating retrieval quality
Approach: Normalize dynamic content into generic knowledge nodes, Preserve content-type and relationship metadata, Combine vector similarity with metadata filters, Keep indexing inspectable, not a black box, Track retrieval sources so answers can cite what they saw
Strengths: Content-type-agnostic normalization (new CMS types need zero retrieval code changes), Keyword-scoring fallback when the vector store is unavailable, so retrieval degrades gracefully instead of failing

Full description

I work with retrieval-augmented generation systems that index structured and unstructured content, retrieve relevant context and generate grounded answers. My focus is not only on embeddings, but also on chunking, metadata, filtering, relationships, evaluation and retrieval transparency.

Capability statement

I design retrieval pipelines where grounding, not just embedding similarity, is the thing being optimized for.

Current focus

Making sure new content types (Expertise, Skills) index and retrieve correctly without any change to the normalization or embedding code.

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Keep exploring

Explore with Alex OS

Alex builds RAG pipelines where the priority is answer grounding — not just embedding similarity — with retrieval that stays inspectable and degrades gracefull…

RAG & Knowledge Retrieval

Alex designs retrieval-augmented generation systems that index structured and unstructured content, retrieve relevant context, and generate grounded answers. The work spans chunking strategy, metadata filtering, relationship preservation, evaluation, and retrieval transparency — not just vector embeddings.

How the retrieval pipeline is built

The core tradeoff: embedding similarity vs. grounding

Most RAG implementations optimize for nearest-neighbor retrieval and stop there. Alex's approach treats grounding as the primary objective — meaning the system is designed to surface contextually accurate, source-traceable answers, not just semantically close chunks. This shifts design decisions toward metadata, filtering, and evaluation rather than model tuning alone.

Verified strengths

Content-type-agnostic normalization: new CMS types (such as Expertise and Skills) index and retrieve correctly without any change to normalization or embedding code. Graceful degradation: a keyword-scoring fallback activates when the vector store is unavailable, so retrieval continues rather than failing. Both are in active use on this site's own knowledge layer.

Connected work: grounding in practice

The RAG answer grounding test (experiments) directly measures the accuracy and trust difference between grounded and ungrounded answers — a practical benchmark for the pipeline decisions described here. The schema-driven CMS architecture (thinking) explains why content-type-agnostic normalization works: a generic content engine means retrieval code never needs to know what type it's indexing.