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  Courses      Module 1     Module 2      Module 3     Module 4     Module 5     Module 6    

Each module corresponds to a major component of the Jarvid pipeline and incrementally builds toward a fully functional deployment.

Module 1: Data Ingest

Objective: Understand the process of gathering and storing raw input data.

• Topics:

  • Sources of data (e.g., databases, APIs, file uploads).

  • Real-time vs batch ingestion pipelines.

  • Implementing data ingestion using cloud services (e.g., AWS S3, Kinesis).

• Activities:

  • Build a script to pull data from an example database.

  • Store ingested data in a cloud storage service.

Module 2: Data Pre-Processing

Objective: Clean, format, and prepare raw data for downstream tasks.

• Topics:

  • Text cleaning and normalization (removing duplicates, handling special characters).

  • Tokenization: Splitting text into meaningful units for embeddings.

  • Managing large datasets for efficient processing.

• Activities:

  • Pre-process employee profiles in a sample dataset.

  • Tokenize text and prepare it for embedding generation.

Module 3: Embedding

Objective: Generate and use embeddings to represent text as numerical vectors.

• Topics:

  • What are embeddings?

  • Techniques for generating embeddings (e.g., using OpenAI or other APIs).

  • Searching in vector space (cosine similarity, FAISS).

• Activities:

  • Generate embeddings for the processed dataset.

  • Use FAISS to build a vector database and perform simple searches.

Module 4: Retrieval-Augmented Generation (RAG) and Prompting

Objective: Implement RAG to augment LLM responses with relevant context from the vector database.

• Topics:

  • Overview of RAG and why it’s important.

  • Using embeddings for knowledge retrieval.

  • Effective prompt design for LLMs.

• Activities:

  • Integrate FAISS with an LLM API.

  • Create LLM prompts that incorporate retrieved data.

  • Example: Retrieve employee expertise data and generate contextual summaries.

Module 5: Deployment

Objective: Build and deploy the entire pipeline as a production-grade API.

• Topics:

  • Designing APIs for LLM-based interactions.

  • Deployment tools: Docker, AWS Lambda, or other platforms.

  • Ensuring scalability and reliability.

• Activities:

  • Build a Flask/FastAPI application for Jarvid.

  • Containerize the application using Docker.

  • Deploy to AWS Lambda or a similar platform.

Module 6: Testing and Optimization

Objective: Ensure the pipeline is reliable, efficient, and optimized.

• Topics:

  • Testing LLM responses (relevance, accuracy).

  • Performance testing of vector searches.

  • Monitoring and logging for deployed systems.

• Activities:

  • Write test cases for API responses.

  • Measure latency of embedding retrieval and LLM generation.

Practical Project: Building Jarvid

Objective: Apply all course concepts by building Jarvid step by step.

• End Goal:
  A functional API that:

  1. Retrieves expertise from a vectorized employee database.

  2. Summarizes it into actionable insights using LLMs.

Milestones:

1. Ingest employee data.

2. Pre-process and tokenize the data.

3. Generate embeddings and set up a vector database.

4. Implement RAG for querying the database.

5. Deploy and test the API.

Learning Outcomes

By the end of the course, learners will:

1. Understand the architectural components of LLM-powered pipelines.

2. Build scalable, production-ready systems integrating LLMs with external knowledge.

3. Design effective prompts for RAG-based applications.

4. Deploy and test their pipelines in a cloud environment.