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Which technique is used to supply domain-specific knowledge to an LLM?
A. Fine-tuning the model on general data
B. Prompt template expansion
C. Domain-adaptation training
D. Retrieval-Augmented Generation
Answer: C
NEW QUESTION # 15
You want to assign urgency and sentiment categories to a large number of customer emails. You want to get a valid json string output for creating custom applications. You decide to develop a prompt for the same using generative Al hub.
What is the main purpose of the following code in this context?
prompt_test = """Your task is to extract and categorize messages. Here are some examples:
{{?technique_examples}}
Use the examples when extract and categorize the following message:
{{?input}}
Extract and return a json with the following keys and values:
- "urgency" as one of {{?urgency}}
- "sentiment" as one of {{?sentiment}}
"categories" list of the best matching support category tags from: {{?categories}} Your complete message should be a valid json string that can be read directly and only contains the keys mentioned in t import random random.seed(42) k = 3 examples random. sample (dev_set, k) example_template = """<example> {example_input} examples
'---'.join([example_template.format(example_input=example ["message"], example_output=json.dumps (example[ f_test = partial (send_request, prompt=prompt_test, technique_examples examples, **option_lists) response = f_test(input=mail["message"])
A. Evaluate the performance of a language model using few-shot learning
B. Train a language model from scratch
C. Generate random examples for language model training
D. Preprocess a dataset for machine learning
Answer: A
NEW QUESTION # 16
What is the primary function of the embedding model in a RAG system?
A. To generate responses based on retrieved documents and user queries
B. To encode queries and documents into vector representations for comparison
C. To store vector representations of documents and search for relevant passages
D. To evaluate the faithfulness and relevance of generated Answers
Answer: B
Explanation:
In a Retrieval-Augmented Generation (RAG) system, the embedding model plays a crucial role in encoding textual data into vector representations, facilitating efficient retrieval and comparison.
1. Function of the Embedding Model:
* Vector Encoding:The embedding model transforms both user queries and documents into high- dimensional vector representations. This numerical encoding captures the semantic meaning of the text, enabling the system to assess similarities between different pieces of text effectively.
* Facilitating Retrieval:By encoding text into vectors, the system can perform efficient similarity searches within a vector database, identifying documents or passages that are most relevant to the user's query.
2. Importance in RAG Systems:
* Semantic Matching:The vector representations allow the system to match user queries with relevant documents based on semantic content rather than mere keyword overlap, enhancing the relevance of retrieved information.
* Efficiency:Vector-based retrieval is computationally efficient, enabling rapid identificationof pertinent information from large datasets, which is essential for real-time applications.
3. Application in SAP's Generative AI Hub:
* Integration with HANA Vector Search:SAP's Generative AI Hub integrates embedding models with HANA's vector search capabilities, allowing for efficient storage and retrieval of vector embeddings.
This integration supports the development of RAG systems that can effectively utilize SAP's data assets.
* Generative AI Hub SDK:SAP provides an SDK that facilitates the implementation of embedding models within RAG systems, enabling developers to encode queries and documents into vector representations seamlessly.
NEW QUESTION # 17
What is a Large Language Model (LLM)?
A. A database system optimized for storing large volumes of textual data.
B. A gradient boosted decision tree algorithm for predicting text.
C. An Al model that specializes in processing, understanding, and generating human language.
D. A rule-based expert system to analyze and generate grammatically correct sentences.
Answer: C
NEW QUESTION # 18
Which of the following executables in generative Al hub works with Anthropic models?
A. GCP Vertex Al
B. AWS Bedrock
C. SAP AI Core
D. Azure OpenAl Service
Answer: B
NEW QUESTION # 19
......
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