Unlock your team by setting the right architecture from the beginning to avoid technical debt, and modern challenges of AI like accuracy, latency, cost, bias, hallucination, and user experience.
Retrieval Augmented Generation (RAG) Architecture
Use your company's corpus of knowledge to customize LLMs and provide relevant information to users with high accuracy and speed.
knowledge base
Embeddings
References
Data Managment
Model Customization
Depending on cost, familiarity, compelxity, and value for your organization, there are a few options available:
Prompt engineering
Retrieval Augmented Generation (RAG)
Fine tuning
AI Data Management
Storage: Structured and unstructured data
Operational databases: SQL, NoSQL, document, graph, vector
Analytics and data lake: Search, streaming, batch, interactive
Data integration: Capture, transformation, streaming
Data governance: Catalog, quality, privacy, access controls
Gen AI
Product Life Cycle
Scoping
Model selection
Adapt or align model
Evaluate
Application integration
LLMOps
LLMOps Pipeline
Data preparation
Pipeline design
Configuration and workflow
Pipeline execution
Deploy LLM
Prompting and predictions
Responsible AI
Holistic Evaluation of Language Models (HELM)
Accuracy
Calibration
Robustness
Fairness & Bias
Toxicity
Efficiency
© Milad Toliyati 2024