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X8 CORE DEVOPS
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KNOWLEDGE BASE

How to Build AI-Integrated Software

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Table of Contents — English

Three Core Approaches to AI Integration

  • LLM API integration: using ready-made models via OpenAI, Anthropic Claude, or Google Gemini APIs
  • Custom machine learning model: developing a model trained on company-specific data
  • Hybrid approach: enriching a ready-made LLM API with company data (RAG — Retrieval Augmented Generation)

The Technical Process of AI Integration

Integrating AI into software begins with clarifying the use case (text generation, classification, prediction, recommendation system). This is followed by data preparation, model/API selection, building the integration layer (usually via REST API), and connecting results into the workflow.

Cost and Time Management in AI Integration

LLM API integration (e.g. a chatbot or content generation tool) can be completed in 4-8 weeks, while developing a custom machine learning model can take 3-6 months due to data collection and training processes. Cost varies based on API usage fees, development time, and data preparation.

FREQUENTLY ASKED QUESTIONS

The fastest way is integrating LLM APIs like OpenAI or Claude into the existing system via REST API. More customized needs may require a RAG architecture or custom model training.

RAG is an architecture that lets an LLM model access a company's own data (documents, database) to produce more accurate and context-specific responses.

OpenAI GPT, Anthropic Claude, and Google Gemini are the most commonly chosen LLM APIs; the choice should be based on cost, performance requirements, and data privacy policies.

The API provider's data retention policies should be reviewed; for sensitive data, an on-premise model or enterprise API agreements (zero data retention) should be preferred.

A simple LLM API integration takes 4-8 weeks, a RAG-architecture system 2-4 months, and projects requiring custom model training 3-6 months.

Key Takeaways

  • AI integration can be done through three core approaches: LLM API usage, custom model training, or RAG architecture.
  • LLM API integration is the fastest and lowest-cost AI integration method.
  • RAG architecture enriches a general LLM model with company-specific data to produce more accurate results.
  • Data privacy policies are a critical decision factor in choosing an API provider.
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