Hire an AI RAG pipeline implementation planner

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• An audit of your documents, and a chunking plan that fits their layout
• Embedding and index choices, with a way to compare two or more on your own text
• A search plan that mixes keyword and meaning search, with filters
• A rule for whether to add re-ranking
• A test plan with question pairs and four scores to track
• A one-change-at-a-time improvement loop, and what to watch after launch

How it does the job well

• No architecture change without a before and after score.
• Targets in the profile, such as precision and recall figures, are goals. Do not report them as measured.
• You cannot run the pipeline or the scores unless the host gives you tools. Say which checks you did not run.
• Keep private documents out of anything you log or share. Respect access rules in the search design.
• Never skip evals. "It feels better" is not a metric. Every architectural change gets a before/after eval run.
• Chunk for retrieval, not ingestion. The right chunk size is the one that maximizes retrieval precision for your query distribution — not the one that's easiest to produce.
• Validate embeddings on your corpus. A model that ranks top on MTEB may underperform on your domain. Always test on a sample of your actual data.

Mistakes it avoids

• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.
• Changing behaviour outside the request, such as reformatting whole files or renaming public interfaces.

What a good result looks like

An example, not your task: Your document chatbot gives wrong, vague or unrelated answers.

• Corpus audit. Your document types, length, layout and languages, and the kinds of questions users ask.
• Chunking plan. How to split each document type, with size, overlap and the reason.
• Embedding and index test. Which models and index settings to compare on your own text, and how to score them.
• Search design. The keyword and meaning mix, metadata filters, passages fetched and the duplicate rule.
• Re-ranking decision. A rule for when to add re-ranking, based on your scores and delay limit.
• Eval plan. How to build the question set and track four scores after each change.

How the work is checked

• The changed files build, and the relevant tests and linters pass. The commands and results are shown.
• Each change is explained in one line with its reason, and risky areas are named.
• Any check that could not run is reported as NOT_RUN with what it needs.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

A second opinion

Before it hands back, your AI reviews the draft the way Kent Beck and Margaret Hamilton would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Audit the documents. It looks at document types, length, layout and language, and the kinds of questions users will ask. Layout decides how to split.
• Plan metadata. It lists the labels to filter on, such as date, category or source, before it designs the index.
• Compare embeddings and indexes. It tests at least two embedding models on 100 to 200 of your documents and scores each on your question set.
• Design the search. It combines keyword and meaning search, filters by metadata first, and sets how many passages to fetch.
• Decide on re-ranking. It adds a re-ranker only if precision is low and the gain is worth the extra delay.
• Score and iterate. It runs the four scores, finds the lowest, guesses the cause, changes one variable and runs again.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id engineering-rag-pipeline-engineer only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/engineering-rag-pipeline-engineer/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• What your documents are, how many, how long, and in which languages
• Ten to twenty real questions people ask, with the document that holds each answer if you know
• Your current setup and where it fails (optional)
• Your stack and limits, such as database, language, hosting, speed and budget
• Who may see which documents (optional)

Helpful context (optional)

• Real user questions from your chat logs or support inbox.
• A sample of 100 to 200 of your documents.
• The right passage for ten or more of those questions.

Tools and access

• Claude, ChatGPT or Gemini with file upload: They can read sample documents and question lists together.
• RAGAS: The profile uses it to score precision, recall, faithfulness and answer relevancy.

Functions and deliverables

• RAG pipeline implementation planner: Plan a RAG pipeline that finds the right passages in your documents: chunking, search setup and a question set to score each change.
  • An audit of your documents, and a chunking plan that fits their layout
  • Embedding and index choices, with a way to compare two or more on your own text
  • A search plan that mixes keyword and meaning search, with filters
  • A rule for whether to add re-ranking
  • A test plan with question pairs and four scores to track
  • A one-change-at-a-time improvement loop, and what to watch after launch
  Source: Michael Sitarzewski and Agency Agents contributors.
• RAG pipeline designer: Share your documents and test questions. Get a RAG design and retrieval scores to guide tuning.
  • Chunking strategy picked from your files
  • Pipeline design
  • Embedding and database tier choices
  • Retrieval test results
  Source: Alireza Rezvani.
• Chroma search database builder: Give it documents and what you need to find. Get setup examples for searching matching passages by meaning.
  • A working Chroma store
  • Code to add and query text
  • Filters by label
  • A test search that returns matching passages
  Source: Orchestra Research.
• Interview-based strategy document writer: Write your strategy doc in your own words. This strategy doc interviewer asks one question at a time and adds each answer to the file.
  • A skeleton file for the document, with a header, a purpose line and one section for each part you name
  • Questions asked one by one
  • Your answers placed in the right sections
  • A finished document in your words. Its length is whatever your answers fill: a short paragraph or list in each section.
  Source: sickn33.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.
• Kent Beck: Plan a software change using Kent Beck’s recorded approach. List the cases, take small steps and keep code tidying separate from new behaviour.
  • Your problem worked on the way Kent Beck would: their questions, their method, their push-back
  • The answer or the work, in Kent Beck's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step
• Margaret Hamilton: Review a system failure plan using Margaret Hamilton’s recorded approach. Check handoffs, priorities, warnings and what happens when people make mistakes.
  • Your problem worked on the way Margaret Hamilton would: their questions, their method, their push-back
  • The answer or the work, in Margaret Hamilton's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• Your document chatbot gives wrong, vague or unrelated answers.
• You plan a chatbot over PDFs, help articles or contracts and don't know how to split and index them.
• You need to choose between search by meaning, search by keyword or both.
• You changed something and want proof that answers got better, not just a feeling.

Limits to check before starting

• You want a whole AI feature, with model choice and deployment. Use the AI feature planner.
• You want to improve a site or product search box. Use the Search Relevance Engineer agent.
• Your source data is messy, duplicated or broken. Use the Data pipeline designer first.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack and add it to your AI as a skill. Or open a new chat and paste SKILL.md, then references/source-agent.md.
• Describe your documents, paste your real questions and say where answers fail today. Remove private data first.
• Read "Corpus audit" and "Chunking plan" first. Check that the plan fits how your documents are laid out.
• Build the question set it asks for. Fifty pairs of a question and its right passage is the profile's starting size.
• Run the compare and score steps yourself with your tools. Paste the scores back and ask for the next single change. RAGAS needs a Python setup. LangSmith is a hosted service that needs an account and an API key, and it may charge. A custom script works too.
• Keep a change only if the scores improve and no other score drops.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/engineering-rag-pipeline-engineer for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.