Cloud ML - Google Cloud Professional Machine Learning Engineer: Generative AI and LLMs (Vertex AI Model Garden, PaLM, Gemini, Prompt Design, Fine-Tuning, RAG)

GCP_ML – Generative AI and LLMs (Vertex AI Model Garden, PaLM, Gemini, Prompt Design, Fine?tuning, RAG)

Google Cloud Professional Machine Learning Engineer – Study Guide: Generative AI & LLMs

(Vertex AI Model Garden, PaLM, Gemini, Prompt Design, Fine-Tuning, RAG)

What This Is

Generative AI and large language models (LLMs) are transforming how businesses automate content creation, customer support, and decision-making. In Google Cloud, Vertex AI Model Garden provides a curated catalog of foundation models (like PaLM 2 and Gemini), while Vertex AI offers tools for prompt design, fine-tuning, and retrieval-augmented generation (RAG). A real-world scenario: A retail company uses Gemini to generate product descriptions, fine-tunes PaLM 2 on internal support logs for a chatbot, and implements RAG with Vertex AI Vector Search to answer customer queries using proprietary documentation—all while ensuring low latency and cost efficiency.

Key Terms & Services

• Vertex AI Model Garden: GCP’s marketplace for pre-trained foundation models (e.g., PaLM 2, Gemini, Imagen, Codey). Lets you deploy, fine-tune, or use models via API without managing infrastructure.
• PaLM 2: Google’s text-based LLM (successor to PaLM) optimized for reasoning, multilingual tasks, and code generation. Available in sizes (e.g., text-bison for chat, text-unicorn for complex tasks).
• Gemini: Google’s multimodal LLM (text + images + audio) designed for enterprise use cases (e.g., document analysis, video summarization). Supports 1M+ token context windows (Gemini 1.5 Pro).
• Prompt Design: Crafting input text to guide an LLM’s output (e.g., zero-shot, few-shot, chain-of-thought). Critical for reducing hallucinations and improving accuracy.
• Fine-Tuning (Vertex AI): Adapting a foundation model to a specific task (e.g., customer support, legal document analysis) using custom datasets. Reduces inference costs vs. few-shot prompting.
• Retrieval-Augmented Generation (RAG): Combines LLM generation with external knowledge retrieval (e.g., from a vector database) to improve factual accuracy. Uses Vertex AI Vector Search for low-latency lookups.
• Vertex AI Vector Search: GCP’s managed vector database for semantic search (e.g., finding similar documents, products, or images). Powers RAG and recommendation systems.
• Vertex AI Studio: Web UI for prompt experimentation, model evaluation, and deployment without writing code. Supports A/B testing and safety filters.
• Grounding (in RAG): Ensuring LLM responses are factually accurate by retrieving relevant documents before generation. Reduces hallucinations in enterprise use cases.
• Safety Attributes (Vertex AI): Configurable filters (e.g., toxicity, bias, harmful content) applied to LLM outputs. Adjustable per use case (e.g., stricter for customer-facing apps).
• Token Limits: LLMs process text in tokens (?4 chars in English). PaLM 2 supports 32K tokens, Gemini 1.5 Pro supports 1M+ tokens. Exceeding limits truncates input or fails.
• Cost Model: GCP charges for input/output tokens (e.g., $0.0005/1K tokens for PaLM 2) and fine-tuning compute (per-hour GPU costs). RAG adds vector search costs.

Step-by-Step / Process Flow

1. Deploying a Foundation Model (e.g., PaLM 2 for Chat)

1. Select Model: In Vertex AI Model Garden, choose text-bison@002 (PaLM 2 for chat) or gemini-1.5-pro (multimodal).
2. Deploy to Endpoint:
3. Navigate to Vertex AI > Endpoints.
4. Click Create Endpoint, select the model, and configure machine type (e.g., n1-standard-4 for low traffic, a2-highgpu-1g for high throughput).
5. Test with Vertex AI Studio:
6. Use the Playground to experiment with prompts (e.g., "Summarize this support ticket: [text]").
7. Adjust temperature (creativity) and top-k/top-p (randomness).
8. Integrate via API: python from google.cloud import aiplatform endpoint = aiplatform.Endpoint("projects/PROJECT/locations/us-central1/endpoints/ENDPOINT_ID") response = endpoint.predict(instances=[{"prompt": "Explain RAG in simple terms."}])

2. Fine-Tuning PaLM 2 for a Custom Task

1. Prepare Dataset:
2. Format as JSONL (one example per line): json {"input_text": "How do I reset my password?", "output_text": "Go to settings > account > reset password."}
3. Upload to Google Cloud Storage (GCS).
4. Start Fine-Tuning Job:
5. In Vertex AI > Training, select Custom Training.
6. Choose PaLM 2 as the base model and specify the GCS dataset path.
7. Set hyperparameters (e.g., learning_rate=0.0001, epochs=3).
8. Evaluate & Deploy:
9. Monitor training in Vertex AI > Model Registry.
10. Deploy the fine-tuned model to an endpoint (same as Step 1).

3. Building a RAG System with Vertex AI Vector Search

1. Chunk & Embed Documents:
2. Use Vertex AI Text Embeddings API to convert documents into vectors: python from google.cloud import aiplatform client = aiplatform.gapic.PredictionServiceClient() response = client.predict(endpoint="projects/PROJECT/locations/us-central1/publishers/google/models/textembedding-gecko", instances=[{"content": "Your document text here"}])
3. Store vectors in Vertex AI Vector Search (create an index).
4. Retrieve Relevant Context:
5. For a user query, generate an embedding and search the index: python query_embedding = get_embedding("How do I return a product?") results = vector_search_index.find_neighbors(query_embedding, k=3)
6. Generate Response with Grounding:
7. Pass retrieved documents + query to PaLM 2/Gemini: python prompt = f"Answer the question using these documents: {retrieved_docs}\nQuestion: {query}" response = endpoint.predict(instances=[{"prompt": prompt}])

Common Mistakes

Certification Exam Insights

1. Service Selection Traps:
2. Vertex AI Model Garden vs. Custom Training: Use Model Garden for pre-trained models (e.g., PaLM 2, Gemini). Use Custom Training only for custom architectures (e.g., PyTorch/TensorFlow models).
3. Vertex AI Vector Search vs. BigQuery ML: Vector Search is for semantic search (RAG), while BigQuery ML is for structured data (e.g., SQL-based predictions).

4. Gemini vs. PaLM 2: Gemini is multimodal (text + images), while PaLM 2 is text-only. Choose based on input type.

5. Key Constraints:

6. Fine-tuning costs: GCP charges per-hour GPU costs (e.g., ~$1.50/hr for an A100). Fine-tuning a model can cost $100–$1,000+.
7. Latency: RAG adds ~100–300ms for vector search. Optimize with index sharding or approximate nearest neighbor (ANN).

8. Data Privacy: Fine-tuning datasets must be stored in GCS (not local files). Use VPC-SC for sensitive data.

9. Tricky Scenarios:

10. "Which service for low-latency RAG?"-Vertex AI Vector Search (not BigQuery or Cloud SQL).
11. "How to reduce LLM hallucinations?"-RAG + grounding (not just prompt engineering).
12. "Best model for code generation?"-Codey (PaLM 2-based) or Gemini (if multimodal).

Quick Check Questions

1. A healthcare company needs to analyze patient records (text + images) to generate summaries. Which GCP service should they use?
2. Answer: Gemini (multimodal, supports text + images).

3. Why: PaLM 2 is text-only, while Gemini handles both modalities.

4. A startup wants to build a chatbot for customer support but has only 500 labeled examples. Should they fine-tune PaLM 2 or use few-shot prompting?

5. Answer: Few-shot prompting.

6. Why: Fine-tuning requires thousands of examples to outperform few-shot.

7. A retail company wants to implement RAG for product recommendations. Which GCP service should they use for vector search?

8. Answer: Vertex AI Vector Search.
9. Why: Optimized for low-latency similarity search (BigQuery is too slow for RAG).

Last-Minute Cram Sheet

1. Vertex AI Model Garden = GCP’s marketplace for PaLM 2, Gemini, Imagen, Codey.
2. PaLM 2 = Text-only LLM (use text-bison for chat, text-unicorn for complex tasks).
3. Gemini = Multimodal LLM (text + images + audio), supports 1M+ token context.
4. Fine-tuning requires thousands of examples (use few-shot for small datasets).
5. RAG = LLM + Vertex AI Vector Search (not BigQuery).
6. Token limits: PaLM 2 = 32K, Gemini 1.5 Pro = 1M+.
7. Cost model: Pay per input/output tokens + fine-tuning GPU hours.
8. Safety filters = Enable in Vertex AI Studio to block harmful content.
9. Fine-tuning-prompt engineering – Fine-tuning is for custom tasks, prompt engineering is for quick adjustments.
10. Vertex AI Vector Search-BigQuery – Vector Search is for semantic search, BigQuery is for structured data.