How NeuroLink's prepareRAGTool wires RAG into generate(): extension-based chunking strategy routing, FNV-1a hashing, a 128-dim InMemoryVectorStore, and a dynamically injected search_knowledge_base tool.
Inside NeuroLink's evaluation system: the BaseScorer/BaseLLMScorer/BaseRuleScorer class hierarchy, the PipelineBuilder-driven EvaluationPipeline that runs scorers together, and how batching, factories, and observability hooks fit around it.
How a missing handleToolExecutionStorage call in a native Google Vertex code path can silently drop tool-call history from an agent's memory, and why every native provider must route through the same TelemetryHandler-backed persistence hook.
How NeuroLink's ConversationMemoryFactory picks between an in-memory store and Redis at startup, what the shared IConversationMemoryManager contract guarantees, and how the Redis backend avoids a tool-call/tool-result race condition.
How NeuroLink's ModelPool classifies a provider error into one of six classes before deciding whether a failed member gets a one-minute cooldown or is retired for the life of the instance.
How NeuroLink's `BaseProvider` abstract class unifies OpenAI, Anthropic, SageMaker, and the rest of the provider catalog behind one contract for chat, embeddings, and image generation.
A deterministic gate that scans a draft for call-graph claims built on real symbol names but invented wiring -- "X calls Y", exact return types, a private/public label, a mermaid diagram of functions that never ran in that order -- and why it flags instead of verifying, pairing with a new drafter rule that tells the model to describe responsibility, not wiring.
How NeuroLink turns a raw user message — text, file paths, URLs, or image buffers — into a provider-ready request through five stages: normalization, file processing, multimodal assembly, budget enforcement, and provider adaptation.
How NeuroLink's BaseFileProcessor lifecycle and priority-based ProcessorRegistry route seventeen file processors across document, data, markup, code, media, and archive formats.
Inside NeuroLink's ContextCompactor: how pruning, deduplication, summarization, and sliding-window truncation combine to keep long conversations under the model's context budget.