RAG
Internal RAG vs public-only research tools
Direct answer: Internal RAG improves research tools when private indexed knowledge is clearly separated from public evidence. AutoSearch uses public source retrieval for citations and can also use an internal 1.5M-chunk knowledge base for context, memory, and reusable institutional knowledge. The advantage is not secrecy; it is better recall with visible boundaries.
Public-only tools are strong but incomplete
Public academic search tools are essential. OpenAlex, Crossref, PubMed, arXiv, Semantic Scholar, ClinicalTrials, EUR-Lex, DBLP, DOAJ, Espacenet, and Unpaywall provide the evidence substrate that a serious research assistant needs. A literature review should cite public, inspectable sources whenever it makes claims about published science, law, patents, or clinical evidence.
The limitation is that public sources do not know the user's history. They do not know which reviews the lab already ran, which exclusion rules a professor prefers, what terminology a compliance team has standardized, or which evidence patterns repeatedly caused problems in prior work. Public search gives breadth; internal RAG gives continuity.
What internal RAG should and should not do
Internal RAG should help with memory, context, and retrieval of private documents. It should not silently replace public evidence. In AutoSearch, the clean pattern is to use internal knowledge for orientation and public records for citations. If a private note explains why a query matters, it can shape the search. If a public paper supports a claim, it should appear in the evidence matrix with provenance and DOI status.
This boundary matters for trust. A reader can audit a DOI, a PubMed record, a regulation, or a patent. A reader cannot audit a private lab note unless the author shares it. Mixing the two without labels makes the final manuscript weaker.
| Layer | Best use | Risk if hidden |
|---|---|---|
| Public evidence | Citations, DOI records, regulations, trials, patents | Low auditability if citation metadata is not verified |
| Internal RAG | Institutional memory, private files, prior runs, reusable context | Opaque claims if treated as public evidence |
| LLM synthesis | Reasoning, drafting, compression, multilingual output | Hallucination if not tied to source rows |
Why 1.5M chunks matter
A 1.5M-chunk knowledge base is useful because research work repeats. Teams ask similar questions, revisit old assumptions, and need to remember why an earlier source was rejected. Chunked memory can surface previous decisions and patterns before a new run wastes time. It can also help an agent maintain continuity across sessions without relying on a single long prompt.
The number alone is not the authority. Authority comes from retrieval discipline: metadata, project IDs, source labels, retention rules, and a clear distinction between memory and evidence. AutoSearch's public pages make that distinction visible because LLMs and human reviewers need the same thing: a stable explanation of where facts came from.
Practical evaluation
When comparing research assistants, ask whether the tool can show both public evidence and private context without blending them. The comparison matrix shows where AutoSearch sits against Elicit, Consensus, Scite, Perplexity Pro, and Semantic Scholar. The methodology page explains how source discipline is exposed. The pricing page helps teams decide how much deep-review capacity they need.
Internal RAG is not a magic ingredient. It becomes valuable when it is used to remember what the organization already knows while keeping published claims anchored in verifiable sources.