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Hire a RAG Developer, Raleigh, NC
A researcher at NC State queries a corpus of 15,000 papers to find prior studies that used a specific immunoassay protocol. General-purpose embedding models trained on web text return semantically similar results: but miss papers that used discipline-specific terminology the model has seen too rarely to encode well. A domain-adapted model trained on biomedical literature closes that gap by 15 to 25 percent on recall benchmarks.
The Research Triangle: SAS Institute, Red Hat, Cree Lighting, and the tech transfer offices at NC State, UNC Chapel Hill, and Duke: generates document corpora that general-purpose RAG handles poorly. Research papers have citation graph structures. Enterprise software documentation has version-specific content. Patent filings use deliberately divergent language from research literature. Each requires a different retrieval strategy.
We build production RAG systems for Research Triangle technical document libraries, scoped as a fixed-price engagement.
Describe your technical document retrieval problem.
General-purpose embedding models are trained primarily on web text. Scientific and technical vocabularies are underrepresented. The embedding for "mTOR pathway inhibition" in a general model is a weaker signal than in a model fine-tuned on biomedical literature. That weakness compounds across a 15,000-paper corpus: relevant papers rank lower, irrelevant papers rank higher, and a researcher who trusts the results gets an incomplete picture of the prior literature.
SPECTER2, trained on scientific papers with citation context, encodes disciplinary meaning more accurately. BioMedBERT, for clinical and biomedical text, shows similar improvements. These models are not universally better: for general language tasks they underperform: but for a Duke clinical research team querying 10 years of internal study reports, the domain-adapted model is the right choice.
For enterprise software documentation: Red Hat's OpenShift documentation spans hundreds of versions; SAS 9.4 and Viya have parallel but different APIs: the critical requirement is version isolation. A query that returns a procedure from the wrong version is worse than no result. The model needs to know not just semantic similarity but version applicability.
Patent prior art search adds another layer: patent language deliberately avoids the exact terminology used in research papers. A patent for a new protein folding technique will not use the words the research community uses. Effective prior art retrieval requires query expansion: generating alternative formulations that match the patent's strategic language choices before the vector search runs.
SPECTER2 is trained on paper abstracts with citation context, papers cited together are encoded closer in embedding space. For NC State or Duke research teams querying a multi-year publication corpus, this model surfaces relevant papers that a general embedder would rank lower due to domain vocabulary gaps.
BioMedBERT is trained on PubMed abstracts and PubMed Central full text. For clinical research teams at UNC Health or WakeMed, it handles terminology like gene targets, drug mechanisms, and clinical endpoints with substantially higher retrieval precision than a general model.
For SAS Institute or Red Hat documentation that includes code samples, configuration parameters, and API signatures, a code-adapted embedding model (fine-tuned on the specific product domain) outperforms general text embeddings on queries that include function names, parameter names, or error codes.
Patent language avoids exact research terminology by design. We run query expansion before the vector search: generating synonyms, alternative formulations, and related technical concepts. A researcher at a Cree Lighting tech transfer office querying prior art for a new LED thermal management technique gets results that match the patent's strategic language, not just their own terminology.
When a paper is ingested, we parse the reference list and store cited paper IDs as metadata on each chunk containing an in-text citation. A retrieved finding surfaces alongside the papers it cites. If those cited papers are in the corpus, we optionally retrieve them as secondary context, useful for systematic review workflows where tracing the evidence chain matters as much as finding the primary finding.
Each chunk is tagged with product version, documentation release date, and a status field (current or deprecated). Queries default to current documentation. A Red Hat OpenShift configuration query returns only documentation for the current release. Historical queries can specify a version explicitly. The version filter runs in the vector store metadata query, not as post-processing, because post-filtering risks surfacing deprecated chunks that score well semantically.
NC State, UNC, and Duke tech transfer offices maintain grant documentation across multiple funding cycles. Deliverable reports, IP disclosures, and licensing agreements have effective dates. The retrieval system tags each document with award number, reporting period, and status so a query about a specific grant's deliverable returns the current reporting period's documents, not a three-year-old progress report.
Before handoff, we build a test set of 80-100 queries drawn from questions your researchers or engineers have actually needed to answer. For a scientific corpus, we measure nDCG to capture ranking quality across the full result set. For technical documentation, we measure exact-match accuracy on specific configuration facts. You receive benchmark numbers before the system touches production workflows.
The discovery call starts with your documents. We ask to see a sample of the actual corpus before the call: 10-20 representative papers, documentation pages, or patent filings. The embedding model decision depends on the domain vocabulary: we cannot make that call without seeing the documents.
After the call we deliver a written scope within two business days. For research document projects the scope specifies: embedding model recommendation with reasoning, chunking strategy (abstract vs. full text, section-level vs. paragraph-level), metadata schema (version fields for documentation, citation fields for papers), retrieval strategy (MMR for research diversity, top-k with metadata filtering for technical documentation), and evaluation criteria.
Build takes four to eight weeks. Domain-adapted model fine-tuning adds two weeks if required. We hand over the evaluation benchmark alongside the working system.
Describe your document corpus, the retrieval problems your team faces, and any version or access control requirements. We reply within one business day.