Knowledge File Creation Process
Knowledge File Creation Process
This document describes the generalized steps to convert an instructional module into a knowledge file optimized for use in a custom GPT. The same steps can be applied to other content files.
- Begin with the raw instructional module in Markdown (or plain text).
- Identify the main sections and subsections.
2. Atomize the Content
- Break down large sections into atomic chunks, where each chunk covers one core concept only.
For each chunk, prepend metadata that helps GPT retrieval:
- Intent: One or two sentences summarizing the purpose of the chunk.
- Q/A Examples: 1–2 representative question/answer pairs phrased in natural language.
- Synonyms: Alternate terms and phrasings students might use in queries.
- Retrieval Hooks: Keywords, short phrases, or code tokens strongly tied to the concept.
This metadata improves retrieval accuracy and disambiguation.
4. Preserve the Instructional Content
- Under a “Content” section, paste the original instructional explanation, examples, and code snippets.
- Ensure the chunk still reads correctly in isolation.
Each chunk should be structured as:
## [Chunk Title]
**Intent:** [One-sentence purpose]
**Q/A Examples:**
- **Q:** [Likely query]
**A:** [Concise answer]
**Synonyms:** [comma-separated terms]
**Retrieval Hooks:** [comma-separated keywords]
**Content:** [Original instructional explanation with examples]
6. Export as Markdown
- Save the file as
xxxx_KnowledgeFile.md where xxxx is the name of the original file or the title of the original content.
- Upload to the custom GPT knowledge base.
Summary of Benefits
- Atomic chunks → improve precision and reduce irrelevant retrieval.
- Metadata (Intent, Q/A, Synonyms, Hooks) → provide clear signals for the retriever.
- Preserved instructional content → ensures GPT responses remain accurate and pedagogically useful.