Tutorials Prompt Engineering Tutorial
Embeddings — Complete Guide
Embeddings — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 42 of 100
Embeddings
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 5: RAG Systems
What is this?
Embeddings turn text into dense vectors where similar meaning sits close in space. They power semantic search in RAG.
Why should you care?
PromptVerse indexes every doc chunk with text-embedding-3-small (or tenant-chosen model).
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
const { embedding } = await openai.embeddings.create({
model: "text-embedding-3-small",
input: "Annual plan refund within 30 days"
});
// store { doc_id, chunk_text, vector: embedding } in vector DBWhat happened?
- Same model embeds queries and documents at index time.
- Cosine similarity finds chunks close to the question vector.
Practice next
- Embed two similar sentences and one unrelated.
- Compare cosine similarity scores.
- Embed a user question.
- Try multilingual embedding model.
- Normalize vectors if your DB requires it.
Remember
Vectors capture semantic similarity. Same model for index and query. Chunk before embed.
Index rebuild
New embedding model ships.
Outcome: Re-embed all chunks overnight; search quality jumps.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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