How to Create a Custom GPT with Your Own Knowledge Base
Want a GPT that answers questions using your content—and not random internet guesses? Here’s a practical, end-to-end guide based on a real build: a custom assistant grounded in video transcripts, published for others to use.
1) Requirements & where to start
Plan: You need a paid ChatGPT account to create Custom GPTs.
Builder entry: In ChatGPT, go to Explore → Create a GPT. You can describe what you want in the Create tab, then fine-tune everything in Configure.
2) Define the assistant
Name & purpose: Short and specific (e.g., “Data Science Coach” that gives practical career advice, learning roadmaps, and resources).
Behavior rules: Supportive, concise, accuracy-first; if unsure, say “I don’t know”; avoid vague generalities.
Conversation starters: Seed useful prompts like “How do I start a career in data science?” or “What skills does a data analyst need?”
3) Build the knowledge base
Custom GPTs can reference uploaded files directly. Keep them clean, dated, and relevant.
Supported types: PDF, TXT, Markdown, CSV.
Practical limit: Up to 20 files per GPT. Consolidate and version on your side.
Curation tips:
Prefer smaller, well-titled bundles (e.g.,
2025-01-curriculum-transcripts-1of8.md).Put titles/dates at the top of each file so the GPT can cite clearly.
Remove outdated content or tag it as such.
Example pipeline: turning a channel into files
Collect video IDs & titles
Create a Google Cloud project and enable YouTube Data API v3.
Use a small Python script to pull channel or playlist video titles + IDs into a local file.
Fetch transcripts
Use a transcripts library/API to download each video’s text.
Prepend each file with a header (Title + URL), then the transcript.
Consolidate for upload
Batch ~50 transcripts per file to stay under the 20-file upload cap.
Separate items with a clear delimiter (e.g., a dashed line) for readability.
Tip: Create a simple Python virtual environment before installing libraries; it avoids system-level conflicts and keeps dependencies tidy.
4) Configure capabilities
In the GPT’s Configure tab:
Toggle Web browsing (optional) and Canvas (for rendering HTML/Python outputs).
Enable Code Interpreter if you want formatting, light analysis, or exportable artifacts.
Start conservative; you can add more capabilities later.
5) Write crisp instructions (the real secret sauce)
Give the model explicit, testable rules:
Source preference: “Prefer answers from uploaded files; if the corpus is insufficient, say so or ask for clarification.”
Style: “Be practical and specific; provide step-by-step guidance where relevant.”
Boundaries: “No personal data inference; do not fabricate citations or links.”
Long answers: “Summarize first, then offer an optional deeper dive.”
6) (Optional) Add an action integration
Actions let your GPT do things (e.g., save the chat to Google Docs).
Import an Action schema from a trusted integrator.
In your instructions, define a mini-workflow, for example:
Propose a short title for the doc.
Create the document.
Append Title → Question → Answer.
If content is too long, condense and retry.
First use will prompt the user to authorize the integration—expected behavior.
7) Share & publish
Only me – private sandbox.
Anyone with the link – share with clients, students, or teammates.
Public / Store – discoverable listing once you add a category and a simple privacy policy (what the GPT stores, where actions send data, user choices).
8) Maintenance & updates
Update cadence: Roll up new content into fresh consolidated files (e.g., quarterly).
Changelog: Note what you add/remove so users trust revisions.
Quality checks: Ask “What’s the source?” and “When was this updated?” during tests to ensure it’s grounding correctly.
9) Guardrails you shouldn’t skip
Data minimization: Pass only necessary fields to actions; never echo secrets.
Attribution clarity: If the GPT cites a file, include filename/title and date.
Failure paths: Define what to do when auth fails, a doc is too long, or a file is missing.
Scope: It’s an information assistant—not a substitute for professional advice.
10) Quick launch checklist
Clear name, purpose, and behavior rules
10–20 clean, consolidated source files with headers and dates
Source preference and uncertainty instructions
Capabilities set (Canvas / Code Interpreter / Browsing as needed)
Optional Action with stepwise instructions + summarization fallback
Sharing mode + privacy policy
Test prompts for: retrieval, long answers, errors, and “save to doc”
Bottom line
With a paid account, well-curated files, and a few precise instructions, you can stand up a Custom GPT that reliably answers from your knowledge. Add a simple action (like saving conversations to Docs), publish with a clear privacy policy, and you’ve turned a pile of transcripts into a helpful, shareable expert assistant.