by: Ritesh SinhaPosted on:

Top 21 Must Read Accenture Gen AI Interview Question

Hey Guys, here are some Must Read Accenture Gen AI Interview Question. These questions were asked to one or more persons. I have collated all the questions from various candidates and tried to list down at one place with some answer. You can always use your own answers for the questions. So let us start.

accenture gen ai interview questions
Accenture Gen AI

Q1: What is Retrieval-Augmented Generation (RAG) in Gen AI, and why is it important?

RAG combines information retrieval with generative models to improve the quality of responses. It first retrieves relevant documents from a knowledge base using techniques like vector search, then uses a generative model to synthesize an answer based on the retrieved context. RAG is crucial because it enables models to access and generate more factual, domain-specific responses by grounding their outputs in actual data.

Q2: How does RAG differ from standard LLM-based generation?

Unlike standard LLM-based generation that relies solely on pre-trained knowledge, RAG retrieves real-time or specific information from a database, which the model then uses to generate a response. This combination allows RAG to reduce hallucinations, provide more accurate, domain-specific answers, and adapt to dynamic content.

Q3: What is multi-hop retrieval in RAG, and when is it useful?

Multi-hop retrieval involves sequentially retrieving multiple pieces of context across documents. This approach is useful when answering complex queries that require synthesizing information from multiple sources, like summarizing, explaining relationships, or making logical inferences across documents.

Q4: Can you explain how you would combine RAG with reinforcement learning?

In an RL-enhanced RAG setup, reinforcement learning could fine-tune retrieval or generation processes by optimizing them based on a reward function. For instance, an RL agent could prioritize documents that improve factual accuracy or reduce redundancy, refining retrieval based on relevance and generation accuracy.

Q5: Why are vector databases important in RAG pipelines?

Vector databases are critical because they store high-dimensional embeddings generated by models and allow for efficient similarity search through techniques like Approximate Nearest Neighbor (ANN). They enable fast retrieval of semantically similar documents, which is essential for real-time or low-latency applications in RAG.

Q6: What’s the difference between traditional databases and vector databases?

Traditional databases are optimized for structured data and exact matches, while vector databases are optimized for high-dimensional data and similarity searches. Vector databases index embeddings, enabling fast retrieval based on semantic similarity rather than exact matching.

Q7: Explain the role of Faiss and Annoy in vector databases.

Faiss (by Facebook) and Annoy (by Spotify) are libraries for fast similarity search in high-dimensional spaces. Faiss is more suited for very large datasets as it supports GPU acceleration, while Annoy is optimized for memory efficiency, making it suitable for smaller datasets or environments with limited memory.

Q8: Describe how fine-tuning works with LLMs.

Fine-tuning an LLM involves training the model on a specialized dataset with task-specific examples. This process adjusts the model weights to adapt it to the desired task, making it more accurate for specific applications like sentiment analysis, summarization, or domain-specific question answering.

Q9: What are some common challenges in deploying LLMs?

Challenges include resource constraints due to the high computational requirements, latency concerns in real-time applications, managing hallucinations (when models generate inaccurate responses), and maintaining user privacy, especially when models process sensitive data.

Q10: What are embeddings, and why are they useful in NLP?

Embeddings are dense, low-dimensional representations of words, sentences, or documents. They capture semantic meaning, allowing models to understand similarities and relationships between different textual elements, which is vital for tasks like search, clustering, and recommendation.

Q11: How would you optimize embedding quality for a specific domain?

To optimize embeddings, I’d consider training or fine-tuning an embedding model on domain-specific data. This ensures the embeddings capture nuances relevant to the domain. Additionally, using larger context sizes and considering subword tokenization can improve quality in specialized applications.

Q12: How would you parse complex output from an LLM?

Parsing complex output can be managed using structured prompts (e.g., asking for JSON-formatted output) or using post-processing techniques like regular expressions or natural language parsers. For more structured needs, libraries like Pydantic can help validate and ensure the output adheres to a specific schema.

Q13: How would you handle model output when the LLM fails to follow the format?

I’d implement fallback mechanisms, such as rephrasing the prompt or adding an error-correction step to reprocess outputs. For repetitive failures, fine-tuning or using reinforcement learning with a reward function to encourage format adherence can also help.

Q14: What is few-shot prompting, and how does it differ from zero-shot prompting?

Few-shot prompting provides the model with a few examples in the prompt to guide it on the task format, improving output quality. In contrast, zero-shot prompting involves asking the model to complete a task without examples, which is often less reliable for complex tasks.

Q15: How do you craft effective prompts to minimize hallucinations?

Clear, directive prompts specifying the model’s role (e.g., “Answer only based on provided context”) help minimize hallucinations. Adding constraints like “Answer only if certain” or “Provide source if available” also guides the model to avoid generating unsupported information.

Q16: What is a LangChain agent, and how is it used in an LLM pipeline?

A LangChain agent is an orchestration tool that dynamically selects which tools or models to use based on user input and task requirements. In an LLM pipeline, it can manage retrieval, generation, and other tool-based steps, allowing for complex workflows like RAG-based question answering, code generation, or multi-turn dialogues.

Q17: How do LangChain agents enable complex multi-step workflows?

LangChain agents enable workflows by parsing queries, deciding which tools or models to invoke, and managing the sequence of actions to fulfill the task. For instance, in a RAG pipeline, the agent can handle retrieval, then trigger generation and post-process the response as required.

Q18: What is LangGraph, and how does it differ from LangChain?

LangGraph is an extension or alternative to LangChain that focuses on defining relationships and dependencies between tasks or tools using graph structures. Unlike LangChain, which follows a more linear sequence, LangGraph allows more complex, interconnected task flows, ideal for multi-step and multi-agent applications.

Q19: How would you use LangGraph for building a contextual question-answering system?

I’d define nodes for each task (retrieval, filtering, summarization, generation) and establish dependencies between nodes to create a contextual question-answering graph. This would enable the system to prioritize contextually relevant tasks first and streamline the flow based on the question requirements.

Q20: How would you evaluate the performance of a RAG system?

Evaluation metrics include retrieval precision and recall, generative accuracy, response relevance, and user satisfaction. Human evaluation or domain expert feedback can also be critical, especially for subjective measures like coherence, contextuality, and factual correctness.

Q21: Explain the role of embeddings in powering search capabilities within a RAG system.

Embeddings enable semantic search, where retrieved documents are similar in meaning to the query, not just by keyword. This enhances retrieval accuracy in RAG by finding documents contextually relevant to the question, making it possible for the generative model to generate more relevant responses.