Guides
166 items
- GuideAgentic AI Frameworks
Designing Approval Workflows for High-Stakes Agent Actions
This guide outlines practical steps to design and implement approval workflows tailored for autonomous agents performing high-stakes actions. It addresses workflow architecture, risk assessment, human oversight integration, and monitoring techniques to enhance agent governance and safety.
- GuideData Engineering for AI
Designing DAGs for Complex AI Pipelines
This guide covers best practices and architectural patterns for designing Directed Acyclic Graphs (DAGs) to orchestrate complex AI pipelines. It addresses task dependencies, scaling, error handling, and tooling considerations for data engineers working on production AI systems.
- GuideMLOps & Model Deployment
Detecting Data Drift for Production Models
This technical guide explores methods and tools for detecting data drift in production ML models. It includes implementation examples illustrating statistical, ML-based, and monitoring-driven approaches essential for maintaining model quality.
- GuideRAG Pipelines & Patterns
Document-Level Access Control in RAG Systems
This guide reviews approaches and best practices for implementing document-level access control in retrieval-augmented generation (RAG) systems. It covers permission mapping, content filtering, system architectures, and compliance considerations tailored for enterprise security teams.
- GuideRAG Pipelines & Patterns
Embedding Caching Strategies for Cost Reduction
This guide examines embedding caching methods to reduce operational costs in Retrieval-Augmented Generation (RAG) workflows. It covers caching architecture options, key performance trade-offs, and vendor-specific features impacting embedding reuse and latency.
- GuideMLOps & Model Deployment
Error Handling and Retries in ML Workflows
This guide covers best practices and architectural patterns for implementing effective error handling and retry mechanisms in machine learning production pipelines. It reviews common failure modes, orchestration framework features, and cost-performance trade-offs relevant to enterprise ML operations.
- GuideAI Governance & Compliance
EU AI Act Compliance Roadmap for Enterprises
This guide outlines the compliance requirements under the EU AI Act for enterprises, including prohibited AI practices, high-risk system obligations, and governance mandates such as the General Purpose AI (GPAI) rules. It provides a structured approach to meeting regulatory expectations in the European market.
- GuideModel Evaluation & Benchmarking
Evaluating Embedding Quality: Hit Rate, MRR, and NDCG Explained
This guide explains Hit Rate, Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (NDCG) — three primary metrics for assessing embedding quality in retrieval-augmented generation (RAG) systems. It aims to help evaluation teams understand strengths and limitations of each metric to inform embedding model selection and tuning.
- GuideMLOps & Model Deployment
Event-Driven ML Pipelines with Kafka and Flink
This guide details how to implement event-driven ML pipelines using Apache Kafka and Apache Flink. It covers architectural patterns, integration strategies, and operational considerations for streaming ML workflows in enterprise environments.
- GuideRAG Pipelines & Patterns
Fine-Tuning Embedding Models for Enterprise Domains (Legal, Medical, Code)
This guide explains how to fine-tune state-of-the-art embedding models specifically for enterprise domains such as legal, medical, and source code. It covers dataset preparation, model selection, tuning strategies, and evaluation protocols to improve semantic retrieval accuracy in domain-specific applications.
- GuideRAG Pipelines & Patterns
From RAG to Agentic RAG: A Migration Roadmap
This guide outlines a step-by-step approach for enterprise AI teams to evolve their existing Retrieval-Augmented Generation (RAG) pipelines into Agentic RAG frameworks. It emphasizes architectural changes, integration best practices, and evaluation metrics essential for agentic capabilities.
- GuideAI Cost, FinOps & TCO
Funding the AI CoE: Budgeting, Chargeback, and Showback Models
This guide examines budget strategies and cost recovery models—chargeback and showback—for funding AI Centers of Excellence. It provides finance and IT leaders with frameworks to align AI CoE investments with enterprise financial governance and accountability.
- GuideAgentic AI Frameworks
Graceful Agent Termination: Canceling Running Tasks and Cleanup
This guide addresses the technical considerations and best practices for terminating autonomous agents in production systems, focusing on canceling active tasks and ensuring comprehensive cleanup to maintain system integrity and resource efficiency.
- GuideModel Evaluation & Benchmarking
Hallucination Detection Methods: Self-Consistency, Embedding, and Verifiers
This guide explores three leading techniques for detecting hallucinations in large language models (LLMs): self-consistency, embedding-based methods, and verifier models. Each method’s implementation details, strengths, and limitations are examined to support enterprise AI teams improving model reliability.
- GuideAgentic AI Frameworks
Human-in-the-Loop for Enterprise Agents: Approval Workflows and Escalation Patterns
This guide explores key design practices for integrating human-in-the-loop (HITL) approval workflows and escalation mechanisms in enterprise AI agents. It covers system architecture considerations, common workflow patterns, and risk management to ensure governance and operational safety.
- GuideRAG Pipelines & Patterns
Hybrid Search: Combining Vector Similarity with Keyword Filtering
This guide explains how to implement hybrid search by integrating vector similarity and keyword filtering. It covers technical considerations, retrieval improvements, and best practices for enterprise knowledge applications.
- GuideRAG Pipelines & Patterns
HyDE: Hypothetical Document Embeddings for Better Retrieval
This guide explains the HyDE technique, which uses hypothetical document generation to improve retrieval in RAG systems. It offers a technical overview and step-by-step implementation recommendations for enterprise AI teams aiming to boost knowledge retrieval accuracy.
- GuideRAG Pipelines & Patterns
Implementing GraphRAG with Neo4j and LLMs
This guide walks through implementing the GraphRAG (Graph Retrieval-Augmented Generation) pattern by integrating Neo4j graph databases with large language models. It provides step-by-step instructions and code snippets to build a scalable, knowledge-enriched question-answering system.
- GuideRAG Pipelines & Patterns
Iterative RAG: Retrieval with Feedback Loops
This guide explores iterative retrieval-augmented generation (RAG) techniques using feedback loops to refine responses for complex enterprise queries. It covers architecture patterns, feedback integration, and evaluation methods to enhance retrieval and generation accuracy in multi-step interactions.
- GuideAgentic AI Frameworks
LangGraph Deep Dive: Building Reliable Enterprise Agents
This guide provides a detailed, step-by-step overview of using LangGraph to build stateful, cyclic workflows in enterprise AI agents. It covers LangGraph’s architecture, key components, and practical implementation strategies for reliability and maintainability.
- GuideAgentic AI Frameworks
Managing Agent State Across Sessions: Databases, Checkpoints, and Resumption
This guide explores strategies for managing agent state in long-running AI workflows. It compares storage options like databases and checkpointing techniques, evaluates resumption methods, and offers best practices for engineering resilient agentic systems.
- GuideRAG Pipelines & Patterns
Managing Latency in Agentic RAG Systems
This guide analyzes latency factors in agentic Retrieval-Augmented Generation systems, providing enterprise AI teams with concrete approaches to optimize response times in performance-sensitive environments. It covers architectural considerations, caching, query optimization, and agent orchestration.
- GuideAI Cost, FinOps & TCO
Measuring Pilot ROI: Success Metrics for 90-Day Trials
This guide provides program managers with a structured approach to measuring the return on investment (ROI) for 90-day AI and technology pilot programs. It outlines key success metrics, data collection methods, and evaluation frameworks necessary to assess pilot outcomes in enterprise settings.
- GuideModel Evaluation & Benchmarking
Metrics That Matter for LLMs: Latency, Tokens, Hallucination, Drift
This guide details four critical metrics for managing large language models (LLMs) — latency, token usage, hallucination, and model drift — with a focus on their operational impact and measurement methods for MLOps engineers.