EXPLORE / 06
Terms index
Specialized AI and software-development terms with concise explanations
Entity records
4542 records · 114/114
- 4521
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 - 4522
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 - 4523
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 - 4524
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 - 4525
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 - 4526
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 - 4527
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 - 4528
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 - 4529
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 - 4530
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 - 4531
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 - 4532
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 - 4533
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 - 4534
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 - 4535
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 - 4536
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 - 4537
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 - 4538
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 - 4539
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 - 4540
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 - 4541
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 - 4542
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