EXPLORE / 06
Terms index
Specialized AI and software-development terms with concise explanations
Entity records
5195 records · 130/130
- 5161
InMemorySaver
InMemorySaver is a lightweight, volatile Checkpointer in LangGraph that stores state snapshots in memory during process execution. Ideal for rapid prototyping and unit testing, but unsuitable for production as state clears on restart.
Website pending - 5162
Trajectory Evaluation
Trajectory Evaluation measures agent quality beyond final text outputs by auditing step-by-step tool choices, parameter validity, total step count, loop occurrences, and policy compliance against gold-standard trajectories.
Website pending - 5163
Time Travel Replay
Time Travel Replay leverages checkpointed state snapshots to rewind an agent to past node states, allow parameter or state edits, and fork execution paths. It is widely used for production post-mortems, branch experimentation, and interactive debugging.
Website pending - 5164
Deterministic Boundary
The Deterministic Boundary strictly isolates model predictions from hardcoded system logic. While LLMs propose tool intents, authentication, permission checks, cost limits, and Schema validations are unconditionally governed by code to enforce safety invariants.
Website pending - 5165
Human-in-the-Loop
Human-in-the-Loop (HITL) pauses automated execution before high-risk actions (e.g., transfers, deployments, mutations) to request explicit human review or parameter edits. It prevents unauthorized model operations and ensures system alignment.
Website pending - 5166
Checkpointer
A Checkpointer automatically persists state snapshots after graph node transitions into storage or memory. It enables state resumption, cross-step memory, human-in-the-loop pause/resume, and historical trajectory replay for resilient long-running agent workflows.
Website pending - 5167
Agent State
Agent State is a typed schema serving as the single source of truth during an agent's execution lifecycle. It explicitly segregates user inputs, model proposals, retrieved evidence, tool logs, and error codes, preventing reliance on unstructured chat history.
Website pending - 5168
StateGraph
StateGraph is a core orchestration structure in LangGraph that models agent workflows as explicit directed graphs. Nodes act as functions updating state, while standard or conditional edges govern transition flows, guaranteeing deterministic control and transparency.
Website pending - 5169
Router Pattern
The Router Pattern uses a classifier or LLM router to direct incoming requests to single-purpose deterministic workflows or specialized agents. It offers low latency, high determinism, and straightforward testability for entry-level intent dispatching.
Website pending - 5170
Plan-and-Execute Pattern
The Plan-and-Execute Pattern breaks long-horizon goals into a structured sequence of sub-tasks before handing them to an executor, triggering replanning upon failure or new observations. Compared to ReAct, it stabilizes long-chain task execution and mitigates context drift.
Website pending - 5171
ReAct Loop
The ReAct Loop (Reasoning and Acting) is an agent pattern where the model alternates between reasoning, selecting tool actions, and observing execution results. It dynamically adjusts steps based on intermediate feedback, suitable for open-ended exploration, but requires step limits and loop guards to prevent execution degradation.
Website pending - 5172
Ungrounded Generation
Ungrounded Generation occurs when a language model produces claims that are unsupported by retrieved context passages, leading to hallucinations and factual drift.
Website pending - 5173
Citation Verification
Citation Verification validates generated response citations (such as [S1]) against retrieved source passages, checking whether citation IDs exist and strictly support the generated claims.
Website pending - 5174
Direct Query Search
Direct Query Search passes unparsed user input directly into vector or keyword search indexes, which frequently fails when queries contain contextual pronouns or multi-intent comparisons.
Website pending - 5175
Query Rewriting
Query Rewriting leverages LLMs to transform vague, ambiguous, or conversational user queries into structured, explicit search queries or multiple sub-queries, boosting downstream retrieval recall rates.
Website pending - 5176
Full Re-indexing
Full Re-indexing parses, chunks, embeds, and re-indexes the entire document corpus from scratch whenever data changes. While eliminating stale data artifacts, it incurs high compute costs and service downtime.
Website pending - 5177
Incremental Indexing
Incremental Indexing uses content hashes to re-embed and update only modified document chunks while purging obsolete records, drastically cutting down compute costs and API overhead during frequent knowledge updates.
Website pending - 5178
Normalized Discounted Cumulative Gain
Normalized Discounted Cumulative Gain (NDCG) measures ranking quality by accounting for multi-level relevance scores and logarithmic position decay, providing a comprehensive metric for evaluating search and re-ranking algorithms.
Website pending - 5179
Mean Reciprocal Rank
Mean Reciprocal Rank (MRR) evaluates retrieval system performance by measuring how high the first relevant item appears in search results. It is computed as the mean of reciprocal ranks across a set of query evaluation cases.
Website pending - 5180
Fixed-size Chunking
Fixed-size Chunking splits text strictly by token or character counts with sliding overlaps. It offers high throughput and easy implementation, but risks breaking logical paragraph structures, code blocks, and table data.
Website pending - 5181
Parent-Child Chunking
Parent-Child Chunking index small child chunks to maximize retrieval precision, and maps matched hits back to larger parent document segments for context assembly, balancing precise candidate matching with contextual completeness.
Website pending - 5182
Score Normalization
Score Normalization scales vector and BM25 scores into a standardized 0–1 numerical range before applying weighted sums. While preserving relative score margins, it is susceptible to score distribution skew between different search engines.
Website pending - 5183
Reciprocal Rank Fusion
Reciprocal Rank Fusion (RRF) combines ranked lists from multiple search algorithms without requiring score normalization across different scales. It scores documents by summing the reciprocal of their ranks across lists, providing robust hybrid search performance.
Website pending - 5184
Top-K Similarity
Top-K Similarity directly truncates top candidates purely based on raw vector distance or keyword matching scores. While highly relevant, it frequently retrieves repetitive context passages when indexing overlapping or redundant documentations.
Website pending - 5185
Maximal Marginal Relevance
Maximal Marginal Relevance (MMR) is a re-ranking algorithm designed to balance relevance and diversity. It penalizes redundant candidates that are overly similar to already selected documents, avoiding top-k results that suffer from low informational diversity.
Website pending - 5186
Non-parametric Memory
Non-parametric Memory refers to explicit knowledge stored outside model parameters, such as in vector databases, document repositories, or knowledge graphs. It enables dynamic retrieval during inference, allowing factual updates without modifying model weights.
Website pending - 5187
Parametric Memory
Parametric Memory represents knowledge implicitly stored within neural network weights during pre-training and fine-tuning. While offering rapid access, it remains frozen at training cutoffs, requires computationally expensive retraining to update, and is susceptible to factual hallucination without external context.
Website pending - 5188
Post-filtering
Post-filtering retrieves top similarity candidates first and then filters out restricted or mismatched items downstream. This approach risks recall collapse when top candidates are filtered out, while causing unnecessary compute overhead and potential security leak windows.
Website pending - 5189
Metadata Pre-filtering
Metadata Pre-filtering applies hard scoping constraints (such as tenant ID, permission ACLs, document version, or update timestamp) prior to executing vector or sparse similarity search. It guarantees unauthorized documents never enter candidate pools, enforcing strict multi-tenant access control.
Website pending - 5190
Cross-Encoder
Cross-Encoder feeds a concatenated query-document pair into a single Transformer model to perform joint self-attention across all tokens. It provides significantly higher ranking precision but suffers from high computational latency, making it ideal for second-stage re-ranking rather than initial candidate retrieval.
Website pending - 5191
Bi-Encoder
Bi-Encoder processes query and document independently into vector embeddings, allowing document vectors to be pre-indexed for fast sub-second nearest neighbor search across massive corpora, though at the expense of missing fine-grained token-level cross interactions.
Website pending - 5192
Fine-tuning
Fine-tuning adapts a pre-trained large language model by updating its neural network weights on task-specific dataset. It improves target output formatting, instruction adherence, and domain-specific stylistic behavior, but incurs high retraining costs and cannot guarantee real-time factual accuracy.
Website pending - 5193
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) retrieves relevant external knowledge before passing it into a generative language model to produce grounded responses. It mitigates factual hallucinations stemming from static parametric memory, enables fast knowledge updates, and provides verifiable citation sources.
Website pending - 5194
Sparse Retrieval
Sparse Retrieval uses high-dimensional sparse representations and inverted indexes (such as BM25) to score text matching based on term frequency. It is highly effective for exact keyword lookup, alphanumeric IDs, and specialized terminology, but fails to match semantically equivalent queries using different words.
Website pending - 5195
Dense Retrieval
Dense Retrieval maps text into low-dimensional dense embedding vectors using neural networks and matches content via vector distance metrics. It excels at semantic similarity, paraphrase matching, and conceptual recall, but may miss precise keyword identifiers or exact alphanumeric terms.
Website pending