Guides
166 items
- GuideRAG Pipelines & Patterns
RAG Routing: Directing Queries to Specialized Retrievers
This guide explains retrieval-augmented generation (RAG) routing strategies to direct queries to specialized retrievers in multi-source knowledge systems. It covers architectural considerations, routing methods, and practical implementation details for enterprise AI deployments.
- GuideAI Cost, FinOps & TCO
Rate Limiting and Budget Controls for Agentic Systems
This guide provides enterprise IT and AI leaders with practical strategies to implement rate limiting and budget controls on agentic AI systems. It covers types of rate limits, enforcement mechanisms, budget tracking, and case studies to prevent runaway compute and API costs in autonomous AI workflows.
- GuideModel Evaluation & Benchmarking
Reading Model Cards: What Enterprises Need to Look For
Model cards provide essential metadata about AI models, including capabilities, limitations, and intended uses. This guide explains the critical sections enterprises should analyze to inform model selection, procurement, and risk assessment.
- GuideAI Cost, FinOps & TCO
Real-Time Cost Monitoring for LLM APIs
This guide provides FinOps teams a structured approach to implement real-time cost monitoring for large language model (LLM) APIs. It details the key metrics, tooling options, and best practices to manage and optimize LLM usage costs effectively.
- GuideAI Security
Red Teaming LLMs: Methodologies and Tooling
This guide outlines practical methodologies and recommended tools for security teams conducting red teaming exercises against large language models (LLMs). It covers preparation, testing phases, evaluation, and reporting to identify and mitigate AI security risks.
- GuideAgentic AI Frameworks
Scaling Agents from 10 to 10,000 Concurrent Users
This guide details architectural strategies, infrastructure considerations, and best practices for scaling agentic AI systems to support 10,000 concurrent users. It covers load balancing, state management, orchestration, and monitoring tailored for enterprise-scale deployments.
- GuideAI Security
Scanning Models for Vulnerabilities: Tools and Techniques
This guide explores the landscape of tools and methods for scanning AI models to detect security vulnerabilities. It covers static and dynamic analysis techniques, open-source and commercial tooling options, and best practices for integrating scanning into AI development pipelines.
- GuideAI Security
Securing LLM API Endpoints: Keys, Tokens, and Rate Limiting
This guide covers best practices for securing large language model (LLM) API endpoints using API keys, token management, and rate limiting. It provides a technical overview intended for platform engineering teams responsible for AI infrastructure and security.
- GuideAI Cost, FinOps & TCO
Semantic Caching for LLMs: Reducing API Calls by 80%
This guide details how semantic caching can help enterprises reduce API calls to large language model (LLM) services by approximately 80%. It includes technical explanations, best practices, and implementation examples with open source tools and cloud services.
- GuideRAG Pipelines & Patterns
Semantic caching for RAG: Reducing redundant retrieval
Semantic caching offers a method to reduce repetitive data retrievals in Retrieval-Augmented Generation (RAG) systems by storing and reusing embedding-based vectors. This guide details the architecture, tradeoffs, and deployment considerations for enterprises focused on lowering latency and operational costs in advanced RAG applications.
- GuideMLOps & Model Deployment
Setting Up Alerts for Model Degradation
This guide walks enterprise AI teams through configuring effective alerting systems to detect model performance degradation. It covers key metrics, threshold setting recommendations, and integration considerations for operationalization.
- GuideAI in Financial Services
SR 11-7 for AI Models: Regulatory Expectations
This guide interprets Federal Reserve SR 11-7 guidance for AI models in financial services. It outlines regulatory expectations for model risk management, emphasizing validation, governance, and ongoing monitoring of AI systems in banking environments.
- GuideMLOps & Model Deployment
Structured Logging for LLM Interactions: Prompts, Responses, and Metadata
This guide outlines best practices for implementing structured logging in large language model (LLM) workflows, covering prompt capture, response tracking, and relevant metadata to support debugging, compliance, and observability in enterprise environments.
- GuideAgentic AI Frameworks
Testing Agentic Systems: Simulation, Sandboxes, and Red Teaming
This guide evaluates key testing methodologies for agentic AI systems, focusing on simulation environments, sandbox deployments, and red teaming. It offers enterprise AI teams practical insights for building effective quality assurance processes that address dynamic autonomy and emergent behaviors in agents.
- GuideAgentic AI Frameworks
The Agent Lifecycle: Build, Test, Deploy, Monitor, Retire
This guide outlines the five key stages of the agent lifecycle—build, test, deploy, monitor, and retire—to help enterprise AI teams transition from prototype to production-ready agentic AI solutions.
- GuideAI Risk Management
Third-Party Model Risk: Assessing Vendor Models
This guide provides procurement and risk teams with a structured framework to assess risks associated with third-party AI models. It covers key evaluation criteria, due diligence practices, and ongoing monitoring to manage vendor-related model risks.
- GuideAI Vendor Selection
Third-Party Model Risk Management for AI Vendors
This guide outlines the key considerations and best practices for procurement and risk teams managing third-party AI vendors. It covers risk identification, vendor assessment, contract controls, and ongoing monitoring based on industry standards and regulatory expectations.
- GuideAgentic AI Frameworks
Unit Testing for Agentic Systems: Mock Tools and Simulated Environments
This guide outlines practical steps for QA teams to design and implement effective unit tests for agentic AI systems. It covers the application of mock tools and simulated environments to isolate complex agent behaviors within testing frameworks. The guide aims to provide clarity on tooling options, architectural considerations, and test design strategies specific to agentic systems.
- GuideAI Cost, FinOps & TCO
Using Spot Instances for LLM Inference: Savings and Failure Handling
This guide examines how infrastructure teams can leverage spot instances for large language model (LLM) inference workloads. It quantifies cost savings, explores architectural adaptations for handling interruption risk, and provides best practices for deployment and monitoring.
- GuideAI Security
Vector Database Security: Encryption, Access Control, and Audit
This guide outlines key security practices for vector databases, focusing on encryption methods, access control mechanisms, and auditing capabilities. It targets security teams responsible for deploying or evaluating vector stores in enterprise retrieval-augmented generation (RAG) and knowledge applications.
- GuideMLOps & Model Deployment
Version control for agent prompts and tools
This guide outlines version control strategies tailored for managing AI agent prompts and tools within MLOps workflows. It covers key challenges, recommended versioning systems, branching strategies, and compliance considerations relevant to agent governance and safety.
- GuideAI Governance & Compliance
Writing an Enterprise Agent Usage Policy
This guide outlines the essential components and considerations for drafting an enterprise agent usage policy. It targets legal and compliance professionals tasked with managing the governance and risk of deploying autonomous AI agents within business environments.
- GuideEnterprise AI Readiness & Adoption
Data Strategy for AI Readiness: The Enterprise Blueprint
Discover how enterprises can build AI-ready data strategies with quality, governance, and modern architectures for maximum AI ROI.
- GuideAI Security
Prompt Injection and Jailbreak Prevention: Defense in Depth
Prompt injection is OWASP Top 10's #1 AI vulnerability. This guide presents the defense-in-depth framework used by financial services and healthcare to prevent attacks.