Getting Started with AI Integration

A comprehensive checklist for assessing AI readiness and planning your first LLM integration project.

Welcome to the AI Integration Guide! This article provides a structured checklist to assess whether your business is ready for AI integration and how to plan your first LLM project effectively.

Phase 1: AI Readiness Assessment

Before starting any AI project, evaluate your readiness across these key dimensions:

Data Availability

  • [ ] Documented Use Cases: Have you identified specific business problems AI can solve?
    • Examples: Customer support automation, document analysis, content generation, code assistance
  • [ ] Accessible Data: Do you have domain-specific data to ground AI responses?
    • Knowledge bases, documentation, FAQs, product catalogs
    • Data formats: PDFs, databases, APIs, wikis
  • [ ] Data Quality: Is your data accurate, up-to-date, and well-structured?
    • Remove duplicate or outdated content
    • Ensure proper categorization and tagging

Technical Infrastructure

  • [ ] API Access: Can you make API calls to LLM providers?
    • OpenAI API key, Anthropic API key, or self-hosted models
  • [ ] Backend Capacity: Does your team have Python/Node.js expertise?
    • FastAPI, Express, or similar backend frameworks
  • [ ] Vector Database: Are you prepared to implement vector storage?
    • Pinecone, Weaviate, or pgvector for PostgreSQL

Budget Considerations

  • [ ] Cost Projections: Have you estimated token costs?
    • GPT-5 Turbo: ~$0.01/1K input tokens, $0.03/1K output tokens
    • Claude 3 Sonnet: ~$0.003/1K input tokens, $0.015/1K output tokens
  • [ ] Volume Estimates: Expected requests per day/month?
    • 1K requests/day ≈ $100-500/month depending on complexity
  • [ ] Contingency Budget: 20-30% buffer for experimentation and iteration

Phase 2: Define Your Use Case

Select your first AI project based on impact and feasibility:

High-Impact Starting Points

  1. Customer Support Chatbot

    • RAG system using existing help documentation
    • Expected ROI: 40-60% reduction in support tickets
    • Timeline: 4-6 weeks for MVP

  2. Internal Knowledge Search

    • Search across internal docs, Slack, Notion
    • Expected ROI: 2-3 hours saved per employee weekly
    • Timeline: 3-4 weeks for MVP

  3. Content Generation Assistant

    • Marketing copy, product descriptions, emails
    • Expected ROI: 50% faster content creation
    • Timeline: 2-3 weeks for MVP

Phase 3: Technical Planning

Architecture Checklist

  • [ ] LLM Provider Selection

    • GPT-5 Turbo: Best for complex reasoning, $0.01/1K input tokens
    • Claude 3 Sonnet: Best for long-context (200K tokens), $0.003/1K input tokens
    • Llama 2 70B: Best for cost-sensitive applications (self-hosted)

  • [ ] Embedding Model Choice

    • OpenAI text-embedding-3-small: 1536 dimensions, $0.00002/1K tokens
    • Cohere embed-v3: 1024 dimensions, multilingual support
    • Sentence Transformers (open-source): Free, self-hosted

  • [ ] Vector Database Setup

    • Pinecone: Managed, easiest to start, $70/month for starter plan
    • Weaviate: Open-source, self-hosted, customizable
    • pgvector: PostgreSQL extension, no additional infrastructure

  • [ ] Retrieval Strategy

    • Chunk size: 512-1024 tokens for documents
    • Overlap: 20% between chunks to maintain context
    • Top-k retrieval: Return 3-5 most relevant chunks

Evaluation Framework

  • [ ] Success Metrics

    • Response relevance (human evaluation: 1-5 scale)
    • Retrieval accuracy (did we find the right documents?)
    • User satisfaction (thumbs up/down, CSAT)
    • Business impact (tickets reduced, time saved)

  • [ ] Baseline Measurement

    • Current process metrics before AI implementation
    • Track for 2-4 weeks to establish baseline

Phase 4: Implementation Roadmap

Week 1-2: Foundation

  • Set up LLM API accounts and billing
  • Choose and configure vector database
  • Create data pipeline for document ingestion
  • Implement basic chunking and embedding

Week 3-4: MVP Development

  • Build retrieval system (vector similarity search)
  • Implement basic RAG pipeline
  • Create simple UI for testing (command line or web)
  • Test with 50-100 sample queries

Week 5-6: Evaluation & Iteration

  • Run human evaluation on 100+ test queries
  • Fine-tune retrieval parameters (chunk size, top-k)
  • Implement prompt engineering improvements
  • Add guardrails for safety and accuracy

Week 7-8: Production Launch

  • Set up monitoring (response quality, latency, costs)
  • Implement caching for common queries
  • Create feedback mechanism for users
  • Gradual rollout: 10% → 50% → 100% of users

Common Pitfalls to Avoid

  1. Starting Too Big: Begin with a single, well-defined use case
  2. Ignoring Data Quality: Garbage in, garbage out—clean your data first
  3. No Evaluation Framework: You can’t improve what you don’t measure
  4. Overlooking Cost Controls: Set usage limits and budgets from day one
  5. Skipping Human-in-the-Loop: Always have review processes initially

Next Steps

Ready to move forward? Contact our team for:

  • Free AI readiness assessment
  • Custom architecture design
  • Proof-of-concept development (typically 2-3 weeks)
  • Production deployment support

Related Articles:

  • Choosing the Right LLM for Your Use Case
  • Backend Architecture Checklist for AI Applications
  • Cost Optimization Strategies for AI Projects