You have a large language model (LLM) that was pretrained by using public data.You want the LLM to generate responses that incorporate frequently updated proprietary content, such as internal documents and knowledge bases.You need to recommend a solution to ensure that the LLM uses the most current information without retraining the model.What should you include in the recommendation?
Answer(s): A
Retrieval-Augmented Generation (RAG) is the optimal, industry-standard approach for incorporating frequently updated proprietary data into an LLM's output. It works by connecting a pre-trained LLM to an external, dynamic knowledge base--such as company documents--using vector databases to retrieve relevant context in real- time, reducing hallucinations and eliminating the need for constant, costly retraining.
https://shiftasia.com/community/retrieval-augmented-generation-rag-a-comprehensive-guide-to-smarter-more- accurate-ai
HOTSPOTFor each of the following statements, select Yes if the statement is true. Otherwise, select No.Note: Each correct selection is worth one point.Hot Area:
Box 1: NoNo - Prompt engineering changes how a generative AI model was trained.Prompt engineering does not change how a generative AI model was trained. Instead, prompt engineering changes how a pre-trained model is used during inference.It is a technique for guiding the model's behavior at the moment of input, rather than a method for altering the fundamental knowledge or neural weights created during training.Box 2: YesYes - Prompt engineering focuses on designing clear, concise, and content-rich instructions.Prompt engineering is a specialized, iterative, and increasingly critical skill focused on designing, structuring, and refining natural language inputs (prompts) to guide generative AI models (LLMs) toward producing accurate, relevant, and high-quality outputs. It bridges the gap between raw, vague user queries and actionable AI-generated content by supplying necessary context, constraints, and instructions.Core Focus Areas of Prompt EngineeringEffective prompt engineering aims to minimize AI hallucinations and irrelevant content by concentrating on three main aspects:Clear Instructions: Using explicit, unambiguous language to define the exact task (e.g., "summarize," "rewrite," "code").Concise Phrasing: Striking a balance between necessary detail and avoiding unnecessary "fluff" to prevent confusing the model.Content-Rich Context: Providing sufficient background information, persona, and examples (few-shot prompting) to ensure the AI understands the nuance of the request.Box 3: NoNo - Effective prompt engineering involves maximizing the number of tokens uses in each request.Effective prompt engineering actually focuses on token optimization, which means minimizing the number of tokens to achieve the best result, rather than maximizing them.
https://www.ibm.com/think/topics/rag-vs-fine-tuning-vs-prompt-engineering https://www.ibm.com/think/topics/prompt-engineering-techniques https://developer.ibm.com/articles/awb-token-optimization-backbone-of-effective-prompt-engineering/
HOTSPOTSelect the answer that correctly completes the sentence.Hot Area:
Box: accuracy and relevance of AI-generated responses.Effective prompt engineering increases the _______________________,Effective prompt engineering is the, strategic process of designing, refining, and optimizing inputs to Large Language Models (LLMs) to guide them toward producing accurate, relevant, and high-quality outputs. By providing clear context, specific instructions, and appropriate constraints, prompt engineering directly influences the quality of AI-generated text, code, or images.
https://www.ibm.com/think/topics/prompt-engineering
Box 1: YesYes - Prompt engineering involves specifying guidelines that help generative AI model generate useful and accurate responses.Prompt engineering is the process of designing and optimizing structured inputs to guide generative AI models toward producing accurate, relevant, and consistent responses. By providing clear instructions, context, and constraints, users can bridge the gap between their intent and the model's probabilistic outputs.Box 2: YesYes - The instruction is the part of the prompt that includes examples to show the AI what its response should look like.An instruction in a prompt, particularly when using "few-shot prompting," includes examples to demonstrate the desired style, format, or tone for the AI's response. These examples act as a guide to help the model understand the expected output structure, improving consistency, accuracy, and adherence to specific formatting requirements.Key details regarding instructions and examples:Few-Shot Examples: Providing specific examples (input and desired output) within a prompt helps the AI understand complex formatting or tone requirements.Purpose: Including examples guides the AI to generate targeted results rather than open-ended, potentially off- target responses.Structure: An effective prompt includes a clear, concise directive (the task) followed by examples (the demonstration) to ensure the AI mimics the desired output pattern.Alternative: Conversely, a "zero-shot prompt" requires the AI to perform a task without examples, relying solely on pre-trained knowledge.Box 3: YesYes - The context is the part of the prompt that helps a generative AI model understand which information to use when generating a response.In the world of AI, context acts as the "frame of reference," providing the specific constraints, data, or background necessary to ensure the output is relevant and accurate rather than generic. Beyond defining the information to use, context can also include the persona you want the AI to adopt or the format you need the answer in.
https://www.ibm.com/think/topics/prompt-engineeringhttps://ai.meta.com/learn/how-to-write-effective-ai-prompts-tips-and-examples https://bhavikjikadara.medium.com/what-is-prompt-engineering-how-to-write-effective-ai-prompts- d1a253aac4ae
Your company plans to use generative AI to help build a website that will showcase various existing products.Which capability best describes a benefit of using generative AI for this project? More than one answer choice may achieve the goal. Select the BEST answer.
Answer(s): C
Generative AI (GenAI) can significantly streamline the creation and localization of a product showcase website.Beyond standard word-for-word translation, GenAI tools--particularly those powered by Large Language Models (LLMs)--provide context-aware localization that adapts product descriptions to specific cultural nuances and brand tones.Key Benefits of GenAI for Product DescriptionsContext-Aware Translation: Unlike older machine translation, GenAI understands the "intent" behind phrases, correctly handling idioms and technical terminology relevant to the product category.Tone & Style Adaptation: You can instruct AI to translate a description into a "professional," "creative," or "technical" tone depending on the target audience.Scalability: GenAI can process thousands of product listings simultaneously, reducing time-to-market by up to 90% compared to manual translation.SEO Optimization: Some tools can automatically incorporate localized keywords into the translated description to improve search visibility in different regions.
https://amplience.com/blog/create-personalized-product-descriptions-that-convert-with-ai
Box: can produce new and original contentThe main difference between generative AI and predictive AI is that generative __________.Generative AI creates new, original content--such as text, images, code, or audio--by learning patterns from data. In contrast, predictive AI analyzes historical data to forecast future outcomes, categorize information, or identify trends. Generative AI focuses on "creating," while predictive AI focuses on "forecasting".
https://www.ibm.com/think/topics/generative-ai-vs-predictive-ai-whats-the-difference
Your company sells hiking and camping gear online.You need a generative AI solution that can interact with customers, ask questions about their needs, and escalate to a human agent if necessary.What solution should you use?
Generative AI chatbots function as a primary point of contact in modern customer service, using natural language processing (NLP) and large language models (LLMs) to engage in human-like dialogue rather than following rigid, pre-programmed scripts.
https://cobbai.com/blog/ai-chatbot-for-customer-service
Your company receives thousands of scanned invoices each month.You need to recommend an AI solution that can automatically extract key details, such as invoice numbers, vendor names, and total amounts.What is the best solution to recommend? More than one answer choice may achieve the goal. Select the BEST answer.
Azure Document Intelligence in Foundry Tools is the preferred solution for automatically extracting structured data--such as vendor names, invoice IDs, and total amounts--from invoices, PDFs, and images. It utilizes advanced AI and OCR to convert unstructured documents into structured JSON data.
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