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LLM Solution

Problem Statement

To enhance Architecture Decision Record we want to use a Large Language Model with a dedicated prompt to generate a synthesis with an Enterprise Architect point of View.

Decision Drivers

• SaaS solution
• REST API
• Markdown file support

Considered Options

• Open AI
• Gemini

Decision Outcome

Chosen option: Gemini because it’s a simple SaaS solution with a free plan to quickly test prompt efficiency.

Consequences

File process will integrate an API call to gemini LLM.

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LLM with RAG integration

Problem Statement

The lack of contextual awareness from software system (C4) informations hinders the LLM’s ability to provide accurate and detailed architectural insights.

Decision Drivers

• Enhanced Contextual Grounding
• sImproved Architectural Understanding
• Reduced Ambiguity

Considered Options

• Langchain
• LlamaIndex

Decision Outcome

Chosen option: Langchain acts as a crucial orchestration layer, simplifying the development of LLM-powered applications

Consequences

• Enhanced response accuracy
• Simplified development and Increased flexibility

Analysis

Okay, let’s analyze this ADR from an Enterprise Architect’s perspective and then generate the Mermaid diagram.