Why does Claude forget my domain knowledge?

Claude forgets your domain knowledge because Project knowledge is siloed per Project, capped in size, and accessed through RAG that paraphrases nuance. Switch Projects and the knowledge is gone. Switch to Gemini or ChatGPT and it never existed. A domain knowledge store needs to live above any single Claude Project so every chat in every tool can read from it.

Three structural choices shape this:

1. Project knowledge is bound to one Project. Anything you upload to a Project lives in that Project's knowledge base. Spin up a sibling Project for a related case and the knowledge does not follow. You can re-upload, but that doubles storage and creates two copies that will drift.

2. RAG paraphrases instead of quoting. When project knowledge grows past the context window, Claude switches into Retrieval Augmented Generation mode and pulls in chunks per turn. Retrieval is fast and broadly accurate, but for high-stakes domains (medical, legal, finance) the model often paraphrases rather than citing verbatim, which loses the precision that matters.

3. Knowledge erodes through summarization in long sessions. A 60-turn case review pushes earlier retrievals out of the active window. The summarizer keeps the conclusions and drops the citations, so by turn 40 Claude is reasoning from its own paraphrase of your knowledge base.

The model still sounds confident. It is just no longer grounded.

Domain-knowledge loss is the highest-cost forgetting, because the outputs look right even when they are not:

• Citations decay. Early in the chat Claude quotes the exact memo. Forty turns in, it summarizes from memory and the regulator-facing draft loses its citation chain.
• Vertical nuance disappears. Industry-specific edge cases get smoothed into generic answers, so the output reads like a junior analyst instead of the specialist you trained.
• Audit trails break. When you cannot show which document drove an answer, the compliance team has to redo the work manually.

Anthropic ships two features that move in the right direction.

Project knowledge with RAG is the headline feature. Anthropic explains it in the RAG-for-Projects help article. When your uploads exceed the model's context, Claude automatically retrieves the most relevant chunks. This works well for moderate knowledge bases. Limits: storage is per Project, retrieval quality depends on chunking choices you cannot tune, and there is no source-of-truth audit log.

Claude Sonnet 4.6 and Opus 4.7 with 1M-token context let you stuff much more into a single chat than ever before. Useful for one massive document. Less useful when you have a corpus, because at full window the model still summarizes mid-session and the cost per turn climbs sharply.

These help. They do not give you a real domain knowledge layer.

Domain knowledge for a real practice is corpus-scale: regulatory texts, internal memos, prior cases, exception logs, and a constantly updating policy index. That is not what Project knowledge was built for. It was built for "drop in the brief and chat about it".

Worse, the knowledge cannot easily reach the other tools you also use. You research in Perplexity, draft in ChatGPT, review in Claude, and prepare slides in Gemini. Each one needs the same knowledge, and Claude's Project knowledge cannot serve any of them.

MemoryLake turns domain knowledge into a first-class memory layer that every Claude chat can read from, with provenance intact.

• One knowledge Project for the whole domain. Upload the corpus once. Every Claude chat, in every Claude surface, pulls the same retrievals, with citations preserved.
• 10,000× more context than raw prompting. MemoryLake's retrieval engine reads from billions of tokens and surfaces only what is relevant per turn. You stop paying for re-uploaded files and stop hitting per-Project caps.
• Provenance and version control built in. Every retrieval traces back to the source file, the version, and the time it was added. Compliance teams get the audit trail they need.

MemoryLake hit 94.03% on the LoCoMo long-context benchmark, the top published result as of 2026, with millisecond retrieval and AES-256 end-to-end encryption.

1. Create a Project and load the corpus. Sign in to MemoryLake, open Project Management, click Create Project, and call it for the domain ("FDA submissions 2026"). Upload the full corpus — guidance PDFs, internal memos, prior cases, audit transcripts — through the Document Drive (PDF, Word, Excel, PowerPoint, Markdown, images all supported). Add curated interpretation notes as named entries in the Memories tab.
2. Generate an MCP Server endpoint. Open the MCP Servers tab, click Add MCP Server, name it "Claude domain expert", and click Generate. MemoryLake returns the API key ID, secret, and endpoint URL. Copy the secret immediately — it appears only once.
3. Connect Claude. In Claude Desktop add the MCP entry to your config with the endpoint and Bearer token, then restart. For claude.ai in the browser, paste a one-line pointer into every relevant Project's instructions, and the REST API surfaces the right knowledge slice per turn — with citations.