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Implementation Guides
Step-by-step tutorials for implementing AI in your organization
PIT™ Methodology
Deep dives into Plan → Implement → Test workflows
Best Practices
Proven strategies for successful AI adoption
Case Studies
Real-world examples of AI implementation success
Latest Posts
Why We Still Need Traditional Classifiers
A technical deep dive into why LLMs need specialized traditional models for vision pipelines: YOLO, classifiers, and embeddings.
Batching vs Incremental Runs
A conversational guide to batching vs incremental runs, idempotency, and schema/versioning for reliable data and agentic pipelines.
LangGraph vs LangChain
State graphs, nodes, edges, checkpoints, persistence, and threading—how they fit together and when to use them.
How LLMs “Remember”
Context windows, reflection loops, and vector memory (Chroma, Milvus, BilberryDB) for practical agents.
Social Reflexion
A consent-first layer that routes expertise across teams using reflection data, relevance matching, and notifications—with opt-in privacy and sensitivity filters.
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