AI Engineering and LLM Lifecycle Management
- 14 May 2025
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AI Engineering and LLM Lifecycle Management
- Updated on 14 May 2025
- 1 Minute to read
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Our platform is built with modern AI engineering principles, enabling full lifecycle management of Large Language Models (LLMs) through integration with leading open-source and commercial tools. This ensures our enterprise customers can safely build, deploy, and evolve AI solutions with transparency, performance, and control.
Tools and Framework
Stage | Capability | Tools/Technologies |
|---|---|---|
Model Development | Fine-tuning, prompt engineering, dataset preparation | Hugging Face Transformers, Langchain |
Prompt Orchestration | Modular prompt chaining, contextual memory | LangChain |
Deployment & Serving | Model hosting and vector database integration | Hugging Face Inference Endpoints, Azure OpenAI |
Retrieval-Augmented Generation (RAG) | Connecting LLMs to enterprise data | LangChain, Milvus DB |
Monitoring & Evaluation | LLM observability, tracing, prompt performance tracking | LangFuse |
Feedback Loop | User feedback collection and evaluation | LangFuse |
Governance & Guardrails | Prompt injection prevention, safe output filtering | Guardrails AI, Azure Open AI Services |
AI Lifecycle Support
Prompt & Vector Versioning: We track changes to prompts using Langfuse and using inbuilt capability of Milvus DB.
Feedback-Driven Tuning: Analyst feedback is logged and prompt is modified based on feedback.
Observability: We use LangFuse for prompt-level tracing, response quality analytics, and error diagnostics.
Security & Governance: Prompt injection detection, content filtering, and role-based access controls ensure responsible use.
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