Training

📚 Phase 2: RAIA TRAINING — Simplified Implementation Guide

Goal: Create a high-quality knowledge base so your AI Agent gives accurate, helpful responses. Estimated Duration: ~60 hours Who’s Involved:

• Training Engineer (Primary)

• Agent Engineer (Support)

• Optional: Business Stakeholders (Data Owners)

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🔍 Step 1: Inventory + Prioritize Data Sources

Time: ~8 hours Owner: Training Engineer

Checklist:

• List all relevant data sources: PDFs, Docs, Excel, FAQs, Chat logs, CRM, etc.

• For each source, record:

  • Format

  • Volume

  • Business priority (High/Medium/Low)

  • Access requirements

  • Quality score (1–10)

• Choose top priority sources (start with high-value, easy-to-process)

Deliverables:

• Data source inventory spreadsheet

• Access checklist

• Priority matrix

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🔄 Step 2: Extract + Convert for AI Use

Time: ~16 hours Owner: Training Engineer

Checklist:

• Convert PDFs, Word, and other docs into clean Markdown

• Convert CSV/Excel into structured JSON

• Validate converted content (headers, bullets, spacing)

• Use automated scripts or prompts for consistency (raia supports markdown input)

Tip: Use chunk sizes of 500–2000 characters for better vector performance.

Deliverables:

• Clean, structured .md or .json files

• Conversion scripts or templates

• Runbook for extraction pipeline

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🧠 Step 3: Organize into a Knowledge Base

Time: ~12 hours Owner: Training Engineer

Checklist:

• Group content into categories:

  • Company Info

  • Products/Services

  • Support Docs (FAQs, Troubleshooting)

  • SOPs and Processes

• Add metadata:

  • Title, tags, priority, source, audience

• Ensure consistency in structure and formatting

• Spot-check for gaps, duplicates, outdated material

Deliverables:

• Organized content folders

• Metadata table

• Knowledge map (visual or tabular)

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🧠 Step 4: Upload to Vector Store

Time: ~10 hours Owner: Training Engineer

Checklist:

• Break large docs into chunks with good semantic boundaries

• Add chunk-level metadata (source, title, category)

• Upload to RAIA vector store

• Test search with real queries

• Monitor upload logs and handle errors

Tip: Embed metadata fields for smart filtering and prioritization during retrieval.

Deliverables:

• Uploaded and indexed vector data

• Chunking and upload logs

• Search functionality validation results

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🧪 Step 5: Validate Knowledge + Quality

Time: ~8 hours Owner: Training Engineer

Checklist:

• Test 20–30 sample queries

• Validate:

  • Relevance of top results

  • Factual accuracy

  • Tone and completeness

• Test retrieval performance (aim <2 sec)

• Fix gaps, re-chunk or reformat as needed

Success Metrics:

• 95%+ accuracy on known questions

• 90%+ relevance on search queries

• <2 second average search time

Deliverables:

• Test results + issue log

• Fixes and re-uploaded content

• Final quality report

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📘 Step 6: Document Training Process

Time: ~6 hours Owner: Training Engineer

Checklist:

• Document:

  • Data source list + access info

  • Conversion process (prompts/scripts/tools)

  • Metadata structure and tagging logic

  • Vector store setup and settings

  • QA and testing results

• Create a knowledge transfer guide for the integration team

Deliverables:

• Training documentation packet

• Knowledge transfer slides

• Update/refresh checklist

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Let me know if you'd like this turned into a downloadable Google Doc, Markdown file, or integrated into your Notion/Confluence workspace. Phase 3 (Integration) template is ready when you are.