Define and implement an ETL pipeline by extracting raw use-case data, transforming it into structured themes, deduplicating and merging, and loading it into an analysis workflow.
Use a lightweight on-device LLM paired with a retrieval augmented generation pipeline over vectorized schema metadata to handle interactive user inputs efficiently.
Transform JSON schema fields with metadata into vector embeddings and use a simple LLM to retrieve and fill the right fields, bypassing complex prompt engineering.
Use prompt engineering over streaming transcripts to map free-form conversational descriptions—like “windows look a bit shabby”—to specific, metadata-annotated schema fields in a JSON inspection template.
Implement a chat-based LLM pipeline that processes each turn of streamed natural-language input, identifies relevant fields in a predefined JSON schema, and fills an inspection report in real time.
Use lightweight vectorization of JSON schemas and data dictionaries to map natural language inputs to structured outputs via vector search instead of complex reasoning.
Use UMAP to reduce high-dimensional embeddings (e.g., 784-dim FashionMNIST) into 2D/3D to visualize and identify semantic clusters such as trousers, dresses, and footwear.
Represent tokens, words, documents, or images as high-dimensional vectors and store them in a vector database so an LLM can efficiently traverse and search curated data.
A simple vectorized Retrieval-Augmented Generation pipeline can outperform complex engineered agent solutions for certain AI tasks by efficiently retrieving and incorporating relevant information.
Compute CAC payback by dividing total sales and marketing costs per customer by the average customer lifetime value to estimate months to recoup acquisition costs.
Use an agent evaluation methodology by overloading an LLM with 30–40 distinct tools to observe its decision-making and tool-selection accuracy under heavy tooling conditions.
If you have a high-quality evaluation set, you can iterate on prompts and inference strategies instead of fine-tuning the base model to achieve great user outcomes.
Train or fine-tune large models on specialized domain corpora—like medical literature—to create agents with deep, expert-level knowledge in that field.
Instead of relying solely on external dev tooling, embed tool-calling capabilities directly within the base AI model so it can act as an "intellectual grunt" able to invoke developer-built tools in context.
A pipeline gathering real developer MCP examples to generate vast synthetic tool-calling data, judged by an LLM rubric and refined via reinforcement learning to optimize agentic tool use.
Models at Google DeepMind generate their own synthetic data via reinforcement learning to extend token limits and advance capabilities without external datasets.
