0.1.1-understand-is_a[0.1.1] A taxonomic relationship linking a kg_instance to its kg_class, establishing membership and enabling inheritan
0.1-understand-core[0.1] the core subdomain of llm memory and context engineering (1 concepts)
0.2.1-understand-token_boundary_marker[0.2.1] The positional delimiter marking the transition from input context to output generation; tokens before this ma
0.2.2-understand-input_token_budget[0.2.2] The portion of context_length reserved and available for prompt tokens, retrieved context, and system instruct
0.2.3-understand-output_token_limit[0.2.3] The portion of context_length reserved for generated tokens; equals context_length minus input_token_budget mi
0.2.4-understand-overflow_threshold[0.2.4] The position or token count at which the model behavior shifts from normal processing to truncation or error;
0.2.5-understand-maximum_position[0.2.5] The highest valid integer position index representable by the position_embedding scheme within context_length;
0.2.6-understand-context_capacity_ratio[0.2.6] The ratio of tokens actively processed to total available context_length; quantifies context_utilization as us
0.2-understand-deep_context_window[0.2] the deep_context_window subdomain of llm memory and context engineering (70 concepts)
0.3.1-understand-em_event[0.3.1] A discrete unit of experience stored in episodic memory — a single interaction, task completion, or notable ou
0.3.2-understand-kg_entity[0.3.2] A node in the knowledge_graph representing a distinct thing — concrete object, person, event, or concept — ide
0.3.3-understand-em_salience_weight[0.3.3] Numeric value indicating how memorable or significant an event is; influences consolidation priority and retri
0.3.4-understand-kg_predicate[0.3.4] The label or type of a kg_relationship; defines the semantics of how two kg_entities relate (e.g., is_a, part_
0.3.5-understand-kg_ontology[0.3.5] The schema layer of a knowledge_graph defining allowed kg_entity types (classes), allowed kg_predicate labels,
0.3.6-understand-kg_class[0.3.6] A kg_entity type within the kg_ontology; groups instances that share structural or semantic properties
0.3-understand-deep_long_term_memory[0.3] the deep_long_term_memory subdomain of llm memory and context engineering (24 concepts)
0.4.1-understand-context_window[0.4.1] The finite token sequence an LLM can process in a single forward pass; defines the maximum input + output capa
0.4.2-understand-context_length[0.4.2] Total number of tokens (input + output) an LLM supports; the hard ceiling on how much text can be loaded into
0.4.3-understand-long_term_memory[0.4.3] Information persisted across sessions, tasks, or deployments; requires external storage and retrieval mechanis
0.4.4-understand-working_memory[0.4.4] The transient token budget allocated for active reasoning within a single model call; scratch space for chain_
0.4.5-understand-attention_mechanism[0.4.5] The core operation allowing each token to attend to all others; scaled dot-product attention is the standard.
0.4.6-understand-vector_database[0.4.6] Storage system indexing embeddings for similarity search; backbone of RAG. Examples: Pinecone, Weaviate, Chrom
0.4-understand-llm_memory_and_context_engineering[0.4] the llm_memory_and_context_engineering subdomain of llm memory and context engineering (343 concepts)
0-understand-llm memory and context engineering[0] the llm memory and context engineering library — every skill a certified region of the KB; call number = home : facets
using-llm-memory-and-context-engineeringUse the llm memory and context engineering neurosymbolic module: RAG library, agent brain, growable KB — proof-checked