| name | 0.4.1-understand-context_window |
| description | [0.4.1] The finite token sequence an LLM can process in a single forward pass; defines the maximum input + output capa |
understand-context_window
CALL NUMBER: llm_memory_and_context_engineering.context_window : deep_context_window(13)
DEFINITION: The finite token sequence an LLM can process in a single forward pass; defines the maximum input + output capacity bounded by architecture or inference config
Invoke this skill to understand context_window down to its primitives. The RELATIVE ROOT below is the least-fixed-point closure of everything it bundles from — the full import cone, grouped by the lib each prim comes from. Projected from a prover-typed KB (MAP/SWI-Prolog consistency gate): every reference below resolves.
THE RELATIVE ROOT (the import cone, by lib)
from deep_context_window
- context_boundary (d2): The hard edge position within context where context_overflow triggers if exceeded; exactly context_length minus one for the output region.
- context_resize_event (d2): The runtime mechanism or policy invoked when context_length must accommodate inputs exceeding current capacity; involves reallocation or overflow handling.
- context_scaling_factor (d2): The multiplier relating raw context_length to effective_context; models with improved attention patterns achieve factors closer to one.
- input_token_budget (d2): The portion of context_length reserved and available for prompt tokens, retrieved context, and system instructions before generation begins.
- max_model_tokens (d2): The absolute token ceiling of the model architecture; synonymous with context_length when no additional constraints apply.
- maximum_position (d2): The highest valid integer position index representable by the position_embedding scheme within context_length; equal to max_model_tokens minus one for zero-indexed sequences.
- output_token_limit (d2): The portion of context_length reserved for generated tokens; equals context_length minus input_token_budget minus overhead tokens.
- overflow_threshold (d2): The position or token count at which the model behavior shifts from normal processing to truncation or error; often slightly below context_boundary.
- token_boundary_marker (d2): The positional delimiter marking the transition from input context to output generation; tokens before this marker are provided context, tokens after are to be generated.
- context_capacity_ratio (d3): The ratio of tokens actively processed to total available context_length; quantifies context_utilization as used_capacity divided by max_model_tokens.
- token_headroom (d3): The arithmetic difference between current position or token count and context_boundary; the remaining room before overflow_threshold is reached.
- position_index (d3): Ordinal integer from zero to context_window_boundary representing each token's location in the sequence; each index must be encodable by the position_embedding scheme in use
- sequence_position (d3): The ordinal index of a token within the full context sequence; constrained to be less than or equal to maximum_position.
from llm_memory_and_context_engineering
- context_length (d1): Total number of tokens (input + output) an LLM supports; the hard ceiling on how much text can be loaded into context_window at once
- context_overflow (d1): The failure state where total input exceeds context_window, causing silent truncation or errors depending on the client library
- context_utilization (d1): How effectively the model uses all available context tokens; metrics include attention entropy, retrieval accuracy at various positions, task performance variation with position.
- effective_context (d1): Portion of context_window the model actually leverages for any given generation; often less than the full window due to attention patterns
- flash_attention (d1): A memory-efficient attention algorithm using tiling and recomputation to avoid materializing the full n×n attention matrix in HBM
- kv_cache (d1): The key-value tensors cached during transformer self-attention to avoid recomputing activations for previously-seen tokens
- position_embedding (d1): Encoding that injects positional information into token representations since attention is permutation-invariant. Choices: sinusoidal, RoPE, ALiBi. Affects how context scales.
- ring_attention (d1): Distributed sliding_window_attention across multiple devices with communication-avoiding algorithms for very long sequences
- sliding_window (d1): Fixed-size contiguous subset of recent tokens kept in working_memory as new tokens arrive and old ones are evicted
- truncation_strategy (d1): Policy for removing old context when context_overflow occurs; options include FIFO, importance-weighted, and semantic summarization
- working_memory (d1): The transient token budget allocated for active reasoning within a single model call; scratch space for chain_of_thought and intermediate results
- context_padding (d2): Appending empty tokens to extend context to alignment boundaries; inefficient but sometimes necessary for batch processing
- token_accounting (d2): Tracking token usage across context_building operations to ensure neither context_window nor max_tokens limits are exceeded
- context_caching (d2): Storing computed key-value pairs from prior forward passes to avoid recomputation on repeated or overlapping context prefixes
- prefix_caching (d2): Caching the KV tensors for shared prefixes (system prompt, few-shot examples) so they aren't recomputed per request; reduces latency and compute for repeated contexts.
- backward_recall (d2): Retrieval of past context tokens; constrained by context_window for working_memory and retrieval_mechanism for long_term_memory
CONSUMERS (what needs this)
attention_sinks, in_context_learning, kv_cache, working_memory
Projected from the llm memory and context engineering KB (438 concepts / 213 relations) — consistency-typed by MAP; the facet list after the colon IS the cross-lib dependency web.
(leaf — this is an actual skill.)
