RAG Development Service
Senior engineers building production RAG systems with proper chunking, hybrid search, reranking, and citation rendering.
Most RAG Systems Retrieve the Wrong Documents
RAG (retrieval-augmented generation) is the most common LLM pattern in production today. The pitch is simple: embed your documents, retrieve the relevant ones for each query, stuff them into the prompt, generate. The reality is more complex: most RAG systems retrieve mediocre results, and the LLM gamely answers based on whatever showed up, hallucinating where the retrieval missed.
Chunking is the most-skipped optimisation. Naive 1000-character chunks split sentences and concepts. Better: chunk on semantic boundaries (paragraphs, headings, code blocks) and overlap chunks by 100-200 characters to preserve context. Even better: store the chunk plus parent-document metadata so the LLM has a path to the source.
Hybrid search beats pure semantic search. Vector similarity is great for "find documents about X" but bad for "find documents containing the exact phrase Y". BM25 (keyword search) is the inverse. Combining them — RRF (reciprocal rank fusion) or weighted score — produces dramatically better retrieval than either alone.
Reranking is the highest-leverage RAG improvement. After initial retrieval returns 50 candidates, a cross-encoder reranker (Cohere Rerank, Voyage Rerank, or open-source) scores the top 50 against the query and returns the top 5. The reranker has full attention over both documents and query, unlike embedding similarity which compresses both into vectors. Result: top-5 quality jumps significantly.
AsyncForge has senior engineers shipping production RAG systems. Submit document loaders, chunking strategies, retrieval pipelines, rerankers, evals, or full RAG builds. Light 4 days, Standard 48 hours, Pro 1 day.
What You Get
Smart chunking
Semantic-boundary chunking with overlap, metadata-preserving, hierarchical (parent-child documents) when useful.
Hybrid search
BM25 + embedding similarity combined via RRF. Per-query, not per-document. Significantly better than either alone.
Reranking
Cohere Rerank, Voyage Rerank, or open-source cross-encoder. Top-50 → top-5 with much better quality.
Citation rendering
LLM answers include source citations linked to the original document chunks. Users can verify, not just trust.
Eval suite
Eval set of question-answer pairs with expected sources. Retrieval recall@5, answer faithfulness, answer relevance — all measured in CI.
Multi-modal RAG
When the source is PDFs with diagrams or scanned forms, we use vision-aware extraction (Unstructured, AWS Textract, Anthropic vision).
Technologies We Use
How It Works With AsyncForge
Subscribe
Plan picked.
Submit RAG work
Chunking, retrieval, reranking, evals, full pipelines.
We deliver
Evaluated, cited, production-grade.
Iterate
Unlimited revisions.
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