Guides
166 items
- GuideConversational AI in HR
Conversational AI in HR: From recruiter bots to always-on employee help
A practitioner's guide to deploying conversational AI across the employee lifecycle — covering recruiting, onboarding, benefits, and ongoing support — with vendor archetypes, integration requirements, and the data plumbing mistakes that stall most programs.
- GuideGenerative AI in Regulated Industries
Generative AI for regulated industries: where to start, what to avoid
A governance-aware framework for selecting and deploying Generative AI use cases in financial services, healthcare, and public sector—without stalling on risk or moving faster than controls allow.
- GuideModel Evaluation & Benchmarking
A/B Testing LLM Versions in Production
This guide outlines a step-by-step approach to conducting A/B testing for large language model (LLM) versions in production environments. It covers infrastructure setup, traffic routing, monitoring metrics, and operational considerations for effective model evaluation and gradual rollout.
- GuideFoundation Models
Advanced Prompting for Reasoning Models: Few-Shot, Scratchpad, and Self-Consistency
This guide breaks down advanced prompting techniques for large language models focused on reasoning tasks. It covers few-shot prompting, scratchpad methods, and self-consistency, illustrating each with detailed examples for enterprise AI practitioners.
- GuideAI Cost, FinOps & TCO
Agent Budget Controls: Setting Per-Agent and Monthly Spend Limits
This guide provides FinOps teams with actionable steps to implement budget controls for autonomous AI agents, focusing on setting per-agent and aggregate monthly spend limits. It outlines the rationale, architectural approaches, tooling options, and best practices to achieve cost governance without impairing agentic AI operations.
- GuideAI Governance & Compliance
Agent guardrails: Preventing harmful actions with allow/deny lists
This guide provides a detailed technical approach for enterprise AI teams to implement allow and deny lists as guardrails in agentic AI systems to prevent harmful actions and enforce policy compliance.
- GuideAI Governance & Compliance
Agent Identity and Authentication: Service Accounts and OAuth for Agents
This guide outlines best practices for managing identity and authentication for autonomous AI agents within enterprise environments. It focuses on the application of service accounts and OAuth protocols to secure agents’ interactions with backend systems and APIs, aimed at IAM teams.
- GuideAgentic AI Frameworks
Agent Observability: Tracing, Logging, and Debugging Multi-Step Runs
This guide covers the core practices and tools for achieving observability in AI agents executing multi-step workflows. It focuses on tracing, logging, and debugging techniques tailored to complex agentic AI architectures to aid enterprise buyers and technical leads in maintaining reliability and performance.
- GuideEnterprise AI Readiness & Adoption
AI Upskilling Roadmap for Enterprises
This guide outlines a structured AI upskilling roadmap for enterprises, focusing on role-specific learning paths for executives, platform engineers, data scientists, and business users. It provides actionable recommendations for creating targeted training programs aligned with organizational AI maturity goals.
- GuideModel Evaluation & Benchmarking
Attributing Business Outcomes to AI: Control Groups and Uplift
This guide explains how analytics teams can attribute business outcomes to AI initiatives reliably using control groups and uplift modeling. It covers methodological considerations, experiment design, and practical examples to measure AI-driven value.
- GuideModel Evaluation & Benchmarking
Attributing Revenue to AI: Uplift Studies and Control Groups
This guide provides a technical overview of methods to assign revenue impact to AI initiatives using uplift studies and control group experiments. It targets analytics teams implementing rigorous AI performance attribution to support investment decisions.
- GuideModel Evaluation & Benchmarking
Building a Hallucination Test Suite for Your Use Case
This guide provides a structured approach for QA teams to develop hallucination test suites tailored to enterprise LLM deployments. It outlines steps from defining use-case scope to integrating tests into CI pipelines.
- GuideEnterprise AI Readiness & Adoption
Building an AI Business Case for Leadership
This guide provides a structured approach to build a compelling AI business case for enterprise leadership. It includes cost frameworks, ROI modeling templates, and practical tactics to align AI investments with strategic objectives.
- GuideEnterprise AI Readiness & Adoption
Building an AI Champions Network Across Business Units
This guide outlines key steps and best practices for program managers designing and implementing AI champions networks to accelerate AI adoption and cross-unit collaboration in large enterprises.
- GuideData Engineering for AI
Building Data Pipelines for AI: Batch, Streaming, and Real-Time
This guide breaks down the essential considerations for designing and implementing data pipelines tailored for AI workloads. It covers batch, streaming, and real-time pipeline architectures, key tools, and best practices for enterprise-scale deployment.
- GuideFoundation Models
Chain-of-Thought Prompting: The Complete Enterprise Guide
A detailed step-by-step guide on chain-of-thought prompting for enterprise AI applications. The guide includes clear examples from math, logic, and planning use cases to help platform engineers and AI buyers design reliable reasoning workflows with large language models.
- GuideRAG Pipelines & Patterns
Chunking Strategies for Enterprise Documents: Overlap, Hierarchy, and Semantics
This guide details chunking methods for preparing enterprise documents in retrieval-augmented generation (RAG) pipelines. It compares overlap, hierarchical, and semantic chunking approaches to optimize ingestion, indexing, and retrieval quality.
- GuideMLOps & Model Deployment
CI/CD for ML: Automated Training, Testing, and Deployment
A step-by-step guide for MLOps engineers on implementing continuous integration and continuous delivery (CI/CD) pipelines tailored for machine learning workflows, focusing on automated training, testing, and deployment to production.
- GuideData Engineering for AI
Data Contracts for AI Pipelines
This technical guide explains the role and implementation of data contracts in AI pipelines, helping data engineering teams ensure data quality and consistency across machine learning stages. It details contract types, enforcement mechanisms, integration points, and best practices in enterprise environments.
- GuideAI Security
Detecting Prompt Injection and Abuse in Production
This guide provides security teams with a technical framework for detecting prompt injection and abuse in production AI deployments. It covers threat identification, monitoring techniques, tooling options, and response best practices.
- GuideAI Vendor Selection
Enterprise AI Vendor Selection Roadmap
This guide outlines a systematic approach for enterprise AI buyers to evaluate potential vendors, balancing technical fit, business alignment, and risk management across the selection process.
- GuideModel Evaluation & Benchmarking
Evaluating Reasoning Quality: Process vs. Outcome Metrics (Expanded)
This guide examines comprehensive approaches to evaluating reasoning quality in large language models (LLMs). It contrasts process-oriented metrics with outcome-oriented metrics and presents detailed rubrics to help enterprise AI teams select appropriate evaluation frameworks for reasoning model assessment.
- GuideModel Evaluation & Benchmarking
Evaluating Reasoning Quality: Process vs. Outcome Metrics
This guide examines methods to evaluate reasoning quality in large language models (LLMs) by comparing process-oriented metrics versus outcome-oriented metrics. It details methodologies, practical trade-offs, and recommendations for enterprises assessing reasoning capabilities.
- GuideData Engineering for AI
Federated Learning in the Enterprise: Training Without Centralizing Data
This guide explains federated learning for enterprises in healthcare and finance sectors, focusing on privacy-preserving AI. It covers federated learning architectures, compliance considerations, and technical implementation best practices for secure decentralized model training.