Embedding Strategies

Software development skill, available on Zeplik

Embedding Strategies is a ready-to-run software development skill on Zeplik. Not for building a full RAG pipeline (use rag-implementation). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.

The Embedding Strategies skill loads automatically when your request matches it, or you can invoke it directly by typing /embedding-strategies in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.

What the Embedding Strategies skill can do

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Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.

How the Embedding Strategies skill works

/embedding-strategies

Select and optimize embedding models for vector search applications. The user describes their corpus, domain, and constraints (or pastes sample documents and current chunking code); deliver a model recommendation with rationale, a chunking design, and example pipeline code as chat artifacts. For assembling the end-to-end retrieval-plus-generation system, use rag-implementation.

When to Use

  • Choosing embedding models for RAG
  • Optimizing chunking strategies
  • Fine-tuning embeddings for domains
  • Comparing embedding model performance
  • Reducing embedding dimensions
  • Handling multilingual content

Core Concepts

1. Embedding Model Comparison (2026)

ModelDimensionsMax TokensBest For
voyage-3-large102432000Claude apps (Anthropic recommended)
voyage-3102432000Claude apps, cost-effective
voyage-code-3102432000Code search
voyage-finance-2102432000Financial documents
voyage-law-2102432000Legal documents
text-embedding-3-large30728191OpenAI apps, high accuracy
text-embedding-3-small15368191OpenAI apps, cost-effective
bge-large-en-v1.51024512Open source, local deployment
all-MiniLM-L6-v2384256Fast, lightweight
multilingual-e5-large1024512Multi-language

2. Embedding Pipeline

Document -> Chunking -> Preprocessing -> Embedding Model -> Vector
              |               |                |
      [Overlap, Size]  [Clean, Normalize]  [API/Local]

Recommendation Workflow

  1. Ask for the corpus profile: domain, language(s), document lengths, query style, latency and cost constraints, hosting requirements (API allowed vs local-only).
  2. Shortlist 2-3 models from the table with explicit trade-offs.
  3. Design chunking: size, overlap, and boundary strategy (headings, sentences, code blocks) matched to the model's token limit and the queries' granularity.
  4. Propose a small offline evaluation: a handful of representative queries with expected passages, measured via recall@k on each candidate model.
  5. Deliver the recommendation memo and pipeline snippet as chat artifacts.

Best Practices

Do's

  • Match model to use case: Code vs prose vs multilingual
  • Chunk thoughtfully: Preserve semantic boundaries
  • Normalize embeddings: For cosine similarity search
  • Batch requests: More efficient than one-by-one
  • Cache embeddings: Avoid recomputing for static content
  • Use Voyage AI for Claude apps: Recommended by Anthropic

Don'ts

  • Don't ignore token limits: Truncation loses information
  • Don't mix embedding models: Incompatible vector spaces
  • Don't skip preprocessing: Garbage in, garbage out
  • Don't over-chunk: Lose important context
  • Don't forget metadata: Essential for filtering and debugging

Templates and Detailed Worked Examples

The full template library (chunking implementations, batching and caching code, domain fine-tuning, dimension reduction) lives in references/details.md. Read that file when you need the concrete templates.

Usage

/embedding-strategies $ARGUMENTS

How to use the Embedding Strategies skill

  1. Sign in to Zeplik

    Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Embedding Strategies skill right away.

  2. Describe your software development task

    Ask in plain language, or type /embedding-strategies to invoke the skill directly. Zeplik recognizes the Embedding Strategies skill and applies its method.

  3. Review and refine the result

    Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.

Source and credit

Author
wshobson
License
MIT

Adapted from the open-source wshobson/agents project and tuned to run natively on Zeplik. View source on GitHub.

Frequently asked questions

What is the Embedding Strategies skill?
Embedding Strategies is a ready-to-run software development skill on Zeplik. Not for building a full RAG pipeline (use rag-implementation). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
How do I use Embedding Strategies on Zeplik?
Sign in to Zeplik and ask in plain language, or type /embedding-strategies in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
Which AI model does the Embedding Strategies skill use?
Any model you choose. Zeplik works across every model in one chat, so the Embedding Strategies skill runs on your preferred model for the task.
Where does the Embedding Strategies skill come from?
The Embedding Strategies skill is adapted from the open-source wshobson/agents project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
How much does the Embedding Strategies skill cost?
Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.

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