When naive 500-token fixed-size RAG chunking fails on financial tables, legal cross-references, or multi-section technical manuals, teams search to hire a freelance Advanced RAG programmer to implement Anthropic Contextual Retrieval, Jina Late Chunking, parent-document retrieval, and cross-encoder reranking. I am Aldian Fazrihady, a Senior Freelance AI Application Engineer.
Core High-Precision Retrieval-Augmented Generation Capabilities
- Anthropic Contextual Retrieval (Prompt-Cached Context Prepending): Prepending a concise document-level context summary to every chunk prior to embedding and BM25 indexing—reducing failed retrievals by up to 49% (and 67% when combined with reranking).
- Markdown-Header & Table-Preserving Semantic Chunking: Keeping Markdown/HTML tables, code blocks, and statutory subsections intact with parent-child hierarchical retrieval (`Small-to-Big` context expansion).
- Reciprocal Rank Fusion (RRF) + Cohere Rerank 3.5 / BGE Reranker: Combining dense `pgvector` cosine similarity with PostgreSQL `tsvector` / ParadeDB `pg_search` BM25 lexical matching.
Frequently Asked Questions
Doesn't generating context summaries for 50,000 chunks in Contextual Retrieval cost a fortune?
Not when engineered properly! By passing the full parent document once with Anthropic Prompt Caching (`cache_control: ephemeral`) or Gemini Context Caching, each chunk context costs pennies per thousand pages.
How do I hire you to upgrade our RAG accuracy?
Hire me via Upwork.