1. Introduction to LLMs and their ecosystem
- LLMs are part of AI, machine learning, deep learning, and generative AI.
- Models like GPT, Gemini (Google), and others contribute to this landscape, which is rapidly evolving.
- Gemini is Google’s multimodal LLM able to process and generate text and images with very long context lengths.
- Gemini can be accessed via Google AI Studio, Google Cloud (Vertex AI), or via the open-source Gemma variant.

2. Common Problems with LLMs
- While easy to call via API, LLMs require careful pre- and post-processing.
- Hallucinations: LLMs generate false or outdated information.
- Poor calculation abilities: calculations done by LLMs are often incorrect.
- Outputs may vary in format and quality (free text, JSON, malformed data).
- Costs can escalate quickly with large or frequent queries.
- Latency and slow response times for large inputs like videos.
- Difficult to objectively measure output quality.
- Risk of disclosing personal or harmful content without filters.

3. Solutions and Best Practices
- Use LLM frameworks (e.g., Langchain, Langchain4j, Firebase Genkit, Semantic Kernel) to handle orchestration and processing pipelines.
- Grounding LLM responses by augmenting input with:
- Google Search results for up-to-date info.
- Google Maps for location data.
- Vertex AI Search for private/custom data.
- Employ code execution tools to handle calculation queries by generating and running Python code for accurate results.
- Apply Retrieval Augmented Generation (RAG):
- Chunk and embed documents into vector databases.
- At query time, embed the question and retrieve similar documents for grounding responses.
- Frameworks like Langchain simplify building RAG pipelines.
- Use function calling to integrate external APIs (weather, stocks, etc.) that LLMs call automatically to provide fresh data.
- To structure outputs, specify desired schemas (e.g., with Pydantic) to get reliable, parseable JSON responses.
- Manage expensive calls via context caching (reuse context without resending) and batch generation (send multiple requests simultaneously for cost savings and efficiency).
- Monitor performance and latency using tools like LangTrace to pinpoint slow operations.
- Evaluate output quality with frameworks including Vertex AI Gen AI evaluation, DeepEval, Ragas, Promfool, and TrueLens.
- Implement security frameworks (e.g., LLM guard, Promfool, Guardrails) to:
- Filter harmful or unwanted content.
- Enforce input/output rules (block code, ban topics, enforce language).
- Anonymize sensitive user data before sending to LLM and de-anonymize responses.
- Chain multiple scanners for layered security.

Actionable Items / Tasks:
- Explore and integrate LLM frameworks like Langchain to implement complex workflows involving pre/post processing.
- Implement grounding via search APIs or private data ingestion for accurate, current LLM responses.
- Use code execution tools to ensure accuracy in calculation-based queries.
- Develop or use RAG pipelines with vector embeddings and semantic search to build knowledge-grounded LLM applications.
- Enable function calling in your applications to augment LLM with real-time APIs.
- Define schemas for LLM outputs to enforce structured and reliable data formats.
- Utilize context caching and batch generation strategies to optimize cost and speed.
- Use monitoring tools such as LangTrace to troubleshoot and optimize LLM call performance.
- Adopt evaluation frameworks to quantitatively assess LLM output quality and relevancy.
- Integrate security and safety controls, including anonymization, content filtering, and scanning, to prevent privacy leaks and harmful content delivery.
- Review the speaker’s GitHub repo and provided example code for Langchain, grounding, evaluation, and security implementations to accelerate development.