Interview Preparation1

Interview Preparation1

1. MLOps vs. LLMOps:

MLOps: Focuses on automating and managing the lifecycle of traditional machine learning models (e.g., image classification, regression).

LLMOps: Tailored to the specific challenges of deploying and managing Large Language Models, emphasizing:

Data & Model Size: Handling massive datasets and model parameters. Prompt Engineering: Optimizing input prompts for desired outputs. Continuous Learning & Adaptation: LLMs constantly evolve with new data and feedback. Explainability & Bias Mitigation: Addressing the “black box” nature of LLMs and ensuring ethical use.

Resources:

2. Prompt Engineering:

Definition: The art and science of crafting effective prompts to elicit desired responses from LLMs. Key Techniques: Instruction Following: Providing clear instructions within the prompt. Few-Shot Learning: Providing a few examples to guide the LLM. Prompt Formatting: Using delimiters, keywords, and structure to enhance clarity.

Resources: Prompt Engineering Guide

3. Retrieval Augmented Generation (RAG):

Concept: Enhancing LLM responses by retrieving relevant information from external knowledge sources. Components: Document Retriever: Finds relevant documents based on the query. Contextualizer: Extracts relevant passages from the retrieved documents. LLM Generator: Uses the retrieved context to generate an informed response.

Resources: Blog: RAG: A Comprehensive Guide

4. Chunking Strategies:

Challenge: LLMs have limited input context windows. Solution: Chunking large texts into smaller segments for processing. Strategies: Sentence/Paragraph-Based: Dividing text based on natural boundaries. Sliding Window: Moving a fixed-size window across the text with overlaps. Meaning-Based: Using semantic segmentation to create cohesive chunks. Resources: Blog: Text Chunking for Large Language Models Library: LangChain (provides chunking utilities)

5. Embedding Models:

Purpose: Converting text into numerical vectors, capturing semantic meaning and relationships. Popular Models: Word2Vec, GloVe: Word-level embeddings. SentenceTransformers: Sentence/paragraph-level embeddings. Applications: Semantic Search: Finding similar documents based on meaning. Clustering: Grouping related documents together. Input Representation for LLMs: Transforming text into a format LLMs understand. Resources: Blog: Understanding Embeddings

6. Internal Working of Vector Databases:

Purpose: Efficient storage and retrieval of high-dimensional vectors (e.g., text embeddings). Key Concepts: Approximate Nearest Neighbor (ANN) Search: Quickly finding vectors closest to a query vector. Index Structures: Optimizing search speed (e.g., k-d trees, ball trees, HNSW). Popular Databases: Pinecone: Managed service with high scalability. Faiss (Facebook AI Similarity Search): Open-source library for efficient similarity search. Resources: Blog: Vector Databases Explained

7. Advanced Search Algorithms:

Relevance: Retrieving the most pertinent information from massive datasets. Algorithms: BM25: A probabilistic model considering term frequency and document length. TF-IDF: Statistical measure of word importance in a document collection. Learning-to-Rank (LTR): Training models to rank documents based on relevance. Resources: Blog: Search Relevance Algorithms: BM25 vs. TF-IDF

8. Supervised Fine-tuning of LLMs:

Goal: Adapting pre-trained LLMs to specific downstream tasks and improving performance. Techniques: Instruction Tuning: Training on examples of desired input-output pairs. Parameter-Efficient Fine-tuning (PEFT): Updating a small subset of model parameters for resource efficiency. Prompt Tuning: Optimizing the prompt itself as learnable parameters. Resources: Paper: Finetuned Language Models Are Zero-Shot Learners Library: Hugging Face Transformers

9. Evaluation of LLMs:

Challenges: Evaluating LLMs goes beyond traditional metrics like accuracy. Evaluation Metrics: Perplexity: Measures how well the model predicts the next token in a sequence (lower is better). BLEU, ROUGE: Evaluate the similarity between generated text and reference text. Human Evaluation: Assessing aspects like fluency, coherence, and factual accuracy. Resources: Paper: Beyond Accuracy: Behavioral Testing of NLP Models with CheckList Library: Hugging Face Evaluate

10. Deployment of LLMs:

Considerations: Infrastructure: LLMs require powerful hardware (GPUs/TPUs) and efficient deployment frameworks. Latency: Optimizing response times for real-time applications. Scalability: Handling large user volumes and requests. Monitoring & Maintenance: Ensuring model performance and addressing issues. Resources: Platform: Hugging Face Spaces Framework: Ray (for distributed computing) Blog: Deploying Large Language Models

11. Agent-based Systems:

Concept: Building intelligent agents that interact with their environment and make decisions. LLM’s Role: Powering agents with natural language understanding and generation capabilities. Applications: Chatbots: Engaging in natural conversations with users. Task-Oriented Agents: Completing tasks based on user instructions. Resources: Framework: LangChain (for building agent-based systems with LLMs) Blog: Building LLM-Powered Agents