Claude Projects are not just a way to organise conversations. They are a fundamentally different operating mode — one where Claude carries context, instructions and files across every conversation automatically, without you having to rebuild from scratch each time.
The difference between using Projects correctly and using them like a regular chat is significant. Here is what each approach actually gives you.
Regular chat resets context on every new conversation
Projects carry instructions and files across all conversations
One setup. Every conversation benefits automatically
Regular chat
Re-upload files every conversation
Re-type instructions every time
No memory between sessions
Context resets completely
Best for one-off tasks
Claude Projects
Files available across all conversations
Instructions apply automatically
Memory builds over time
Context persists between sessions
Best for ongoing work
Key insight: If you have done the same type of task more than twice in Claude, it belongs in a Project. That is the rule. One setup pays dividends on every future conversation.
When you add many files to a Project, Claude does not read all of them on every message. It uses RAG — Retrieval-Augmented Generation — to find and pull only the sections most relevant to your question. This is what keeps Projects fast and efficient even as they grow.
Key insight: RAG means Claude is not reading your entire knowledge base on every message. It is retrieving targeted sections. This is why how you name and structure your files matters enormously — RAG finds what it can identify.
How RAG works in a Claude Project
1
You ask a question — Claude receives your message and identifies what it needs to answer it.
2
Retrieval triggers — Claude searches your Project files for sections most relevant to the question, not the entire document.
3
Context is assembled — The retrieved sections, your instructions and your message are combined into Claude's working context.
4
Claude responds — The answer is grounded in your actual Project files, not in Claude's general training.
Practical tip: When asking questions in a Project with many files, reference the specific document by name: "In the Q2 strategy doc, what are the key risks?" This helps RAG retrieve the right sections faster and gives you sharper answers.
RAG is only as good as the files you give it. A well-structured Project with clean, descriptive files outperforms a cluttered one with large, poorly named documents every time. Here is the file strategy that makes the difference.
Key insight: When you upload a 40-page report to a Project, RAG does not read all 40 pages. It retrieves sections. If those sections are buried in vague headings or bloated formatting, RAG misses them. Clean files get better answers.
Do this
Use .txt or .md files — they use fewer tokens than PDFs and are easier for RAG to parse.
Avoid this
Uploading scanned PDFs or image-heavy documents — RAG struggles to extract clean text from them.
Do this
Give files descriptive names: "Q2-2026-budget-variance.md" not "report-final-v3.pdf".
Avoid this
Uploading your entire SharePoint library — only include files Claude will genuinely need.
Do this
Split large documents into topic-focused files — one file per theme, not one giant document.
Avoid this
Keeping outdated file versions in the Project — stale files confuse RAG and produce inconsistent answers.
1
Write sharp Project instructions
Your Project instructions run on every conversation. Write them once, write them well. Include your role, your preferred output format and any standing rules Claude should always follow.
2
Keep the Project scoped
One Project per topic or client, not one Project for everything. Focused Projects give Claude cleaner context and produce sharper answers than a general-purpose catch-all.
3
Update files, do not accumulate them
When a document is superseded, remove the old version. Projects are not archives. They are working environments — keep only what is current and relevant.
4
Name conversations inside the Project
Give each conversation inside a Project a clear name. This makes it easy to find previous work and helps you build a structured knowledge trail over time.