Guides
147 items
- GuideAI Cost, FinOps & TCO
AI Cost Observability: Tagging, Budgets, and Alerts
This guide explains how FinOps teams can implement effective cost observability for AI workloads using tagging strategies, enforce budgets, and configure alerts. It covers best practices for granular AI spend breakdowns and monitoring to control AI project costs.
- GuideAI Security
AI Security Training for Developers: OWASP Top 10 for LLMs
This guide outlines how enterprise AI security training programs can integrate the OWASP Top 10 risks for large language models (LLMs) into developer education. It provides actionable steps for security teams to enhance developer understanding and reduce AI-specific vulnerabilities.
- GuideAI Governance & Compliance
Audit Trails for Agents: Recording Every Decision and Action
This guide outlines best practices for creating comprehensive audit trails in autonomous and semi-autonomous agents, focusing on requirements for compliance and security teams to ensure transparency, accountability, and mitigation of operational risks.
- GuideFoundation Models
Automating document processing with multimodal LLMs
This guide outlines the process of implementing multimodal large language models (LLMs) for automating document processing tasks in enterprise settings. It covers structured and unstructured document types, including invoices, forms, and contracts, highlighting model selection, data preparation, integration strategies, and evaluation metrics.
- GuideMLOps & Model Deployment
Autoscaling LLM Inference: GPUs, Pods, and Queue Management
This guide details best practices and architectural patterns for autoscaling large language model (LLM) inference workloads on Kubernetes clusters. It covers GPU resource management, pod scaling strategies, and queue handling techniques to optimize throughput and latency.
- GuideMLOps & Model Deployment
Batching and queueing for LLM inference: Throughput vs. latency
This guide examines batching and queueing techniques for large language model (LLM) inference workloads, focusing on the trade-offs between throughput and latency. It provides practical advice for enterprise teams managing high-volume LLM deployments, with technical insights into architecture and cost implications.
- GuideModel Evaluation & Benchmarking
Bias and Fairness Testing for Enterprise Models
This guide provides enterprise practitioners a structured approach to bias and fairness testing for AI models, outlining key metrics and practical mitigation strategies relevant to model risk management.
- GuideRAG Pipelines & Patterns
Building a Production RAG Ingestion Pipeline
This guide outlines the key steps and architectural considerations for building a scalable and reliable production pipeline for Retrieval-Augmented Generation (RAG) in enterprise knowledge management. It covers data ingestion, transformation, indexing, and query orchestration.
- GuideAI Cost, FinOps & TCO
Building an AI ROI Dashboard for Executives
This guide provides data teams with a technical framework to design and implement AI ROI dashboards tailored for executive decision-making. It covers key metrics, data sources, architectural considerations, and visualization best practices to align AI investments with business outcomes.
- GuideRAG Pipelines & Patterns
Building an Internal Knowledge Agent for Slack, Teams, and Email
This guide provides enterprise search teams with a step-by-step framework to build an internal knowledge agent integrated with Slack, Microsoft Teams, and Email. It covers architecture considerations, data integration, retrieval-augmented generation (RAG) methods, and user experience design for effective enterprise knowledge workflows.
- GuideAgentic AI in IT Operations
Building an IT Helpdesk Agent: Password Resets, Access Requests, and Ticket Triage
This guide provides IT operations teams with a structured approach to developing an AI-powered IT helpdesk agent. Covering core functionalities including password resets, access requests, and ticket triage, it offers implementation best practices, architectural considerations, and integration tips for enterprise environments.
- GuideMLOps & Model Deployment
Building an LLM observability dashboard
This guide outlines the essential steps for constructing an observability dashboard tailored to large language models (LLMs). It includes example queries and metrics to track LLM performance, cost, and reliability within production environments.
- GuideConversational AI in Customer Service
Building Enterprise Voice Assistants: IVR Replacement with LLMs
This guide outlines the process for enterprise customer experience teams to replace traditional IVR systems with voice assistants powered by large language models (LLMs). It covers technical considerations, architecture design, integration strategies, and evaluation metrics.
- GuideRAG Pipelines & Patterns
Building RAG Agents That Query APIs, Databases, and Internal Tools
This guide provides a structured approach for developers to build Retrieval-Augmented Generation (RAG) agents that effectively interact with external APIs, internal databases, and enterprise tools. It covers key design choices, integration patterns, and best practices for development and deployment.
- GuideAgentic AI Frameworks
Building Reusable Agent APIs: Tool Definitions and OpenAPI Integration
This guide details how platform teams can design reusable agent APIs by defining tools effectively and integrating OpenAPI specifications. It addresses architecture decisions, tooling strategies, and implementation best practices to enable consistent, scalable agent-based automation.
- GuideMLOps & Model Deployment
Canary Deployments for LLMs: Testing New Versions Safely
This guide explores best practices for implementing canary deployments specifically tailored for large language models (LLMs). It covers risk mitigation strategies, infrastructure considerations, and monitoring essentials to help MLOps teams deploy new model versions safely.
- GuideAI Governance & Compliance
CCPA/CPRA: AI and Consumer Opt-Out Rights
This guide explains the implications of the California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA) on enterprise use of AI. It focuses on consumer opt-out rights, compliance challenges, and best practices for integrating these rights into AI workflows.
- GuideComputer Vision
Chart and Graph Understanding: From Pixels to Data
This guide explores methods for extracting and interpreting data from charts and graphs using AI-driven techniques. It covers image processing, multimodal models, and integration into business intelligence workflows to enhance data-driven decision-making.
- GuideRAG Pipelines & Patterns
Code Embeddings for Semantic Code Search
This guide explains the use of code embeddings in semantic code search, detailing embedding types, model options, architecture considerations, and best practices for developer platforms.
- GuideMLOps & Model Deployment
Collecting User Feedback for Model Improvement
This guide outlines practical strategies for product and machine learning teams to capture and utilize user feedback to enhance model performance. It discusses feedback types, collection methods, integration into retraining cycles, and common pitfalls.
- GuideFoundation Models
Confidence scoring: when your LLM should say "I don't know"
This guide explores methods for estimating uncertainty in large language models (LLMs) and the implementation of confidence scoring to reduce hallucinations and improve reliability. It details metrics, calibration techniques, and practical deployment considerations for enterprise AI teams.
- GuideRAG Pipelines & Patterns
Context Graphs for Enterprise RAG: Beyond Simple Retrieval
This guide examines the use of context graphs to enhance Retrieval-Augmented Generation (RAG) in enterprise settings. It details how relationship-aware retrieval improves context precision and reasoning capabilities beyond keyword or vector search alone.
- GuideRAG Pipelines & Patterns
Corrective RAG: Retrieval with Self-Correction and Re-Ranking
This guide explores the architecture and implementation of Corrective RAG—an approach combining retrieval-augmented generation with iterative self-correction and result re-ranking. It targets enterprise AI teams aiming to improve accuracy and relevance in knowledge-intensive applications beyond traditional RAG capabilities.
- GuideAI Governance & Compliance
Data lineage for AI compliance and debugging
This guide explains data lineage's role in AI compliance and debugging, focusing on how governance teams can establish transparent and auditable data flows. It covers best practices, tooling considerations, and integration with MLOps pipelines to mitigate risks and support regulatory obligations.