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Richtige Antwort: BTo ground LLM generation with external dynamic context fetched at inference time
RAG pulls relevant text chunks from external document indexes or databases and injects them into the prompt, mitigating hallucinations and stale training data.
Richtige Antwort: ACosine similarity focuses on directional orientation rather than vector magnitude
Cosine similarity measures the angle between vectors, capturing semantic orientation independently of magnitude (text length), making it ideal for normalized dense embeddings.
Richtige Antwort: BModels retrieve and utilize information located at the beginning and end of long prompts better than the middle
Empirical research demonstrates that transformer attention mechanisms frequently prioritize tokens at the extreme beginning and end of extensive contexts, degrading recall for middle context.
Richtige Antwort: BTo jointly score query-document pairs with full self-attention to refine initial top-k retrieval results
While bi-encoders calculate query and document embeddings separately for fast retrieval, a cross-encoder examines query and document tokens together, yielding superior relevance rankings.
Richtige Antwort: AIt generates a synthetic answer using an LLM, embeds that answer, and searches for similar real documents
HyDE prompts an LLM to generate a hypothetical answer to the user's question, then uses the embedding of that hypothetical text to find semantically aligned real source documents.
Richtige Antwort: BLogarithmic search time complexity for Approximate Nearest Neighbor (ANN) vector queries
HNSW builds multi-layered graphs with skip-list properties, enabling fast logarithmic Approximate Nearest Neighbor (ANN) search through high-dimensional vector spaces.
Richtige Antwort: BCrucial semantic context surrounding facts is severed, leading to incomplete or fragmented answers
Overly aggressive chunking isolates sentences from their surrounding context, meaning retrieved fragments may lack the pronouns, qualifications, or framing needed for accurate synthesis.
Richtige Antwort: BIt splits documents at points where the semantic similarity between consecutive sentences drops sharply
Semantic chunking monitors embedding distance between adjacent sentences and introduces breaks when semantic coherence shifts significantly, keeping topical units intact.
Richtige Antwort: AContrastive language-image pre-training (like CLIP)
Models like CLIP use contrastive learning to push matching text-image pairs close together in the shared embedding space while pulling non-matching pairs apart.
Richtige Antwort: BFaithfulness (Groundedness)
In RAG evaluation frameworks (such as Ragas), Faithfulness specifically quantifies whether every claim made in the output can be directly inferred from the retrieved context.
Richtige Antwort: BExtracting only the sentences directly relevant to the query from retrieved passages to conserve prompt tokens
Contextual compression inspects retrieved documents and strips out irrelevant filler text before passing the condensed, highly relevant content to the generation model.
Richtige Antwort: BIt handles exact keyword matches (product codes, names) alongside broad conceptual semantic queries
Dense vector search excels at high-level semantic meaning but can miss exact alphanumeric matches (part numbers, acronyms), which sparse BM25 keyword search handles reliably.
Richtige Antwort: BTo supply structured JSON schemas that instruct the model how to emit arguments to execute external functions
Tool schemas describe functions, parameter types, and descriptions in JSON format, allowing the LLM to output structured JSON matching the schema when an external action is required.
Richtige Antwort: BFine-tuned models struggle to cite exact sources and suffer from knowledge obsolescence as facts change
Fine-tuning updates internal weights, making factual verification difficult, hallucination harder to trace, and keeping data up-to-date costly compared to updating an external RAG index.
Richtige Antwort: BIt reduces model memory footprint and inference latency with minimal degradation in accuracy
Quantization maps high-precision floating-point weights (FP16/FP32) to lower-bit representations (INT8/INT4), drastically reducing VRAM usage and accelerating memory-bound inference.
Richtige Antwort: BIt merges multiple ranked lists of search results into a single list based on position rather than raw scores
RRF combines rankings from disparate retrieval algorithms (e.g., keyword and vector) by scoring documents based on their inverse rank position, smoothing out incompatible score distributions.