Leverage TensorFlow’s Embedding Projector to display high-dimensional embeddings in 2D or 3D using cosine similarity for exploratory analysis of vector relationships
Use the UMAP project to reduce high-dimensional data (e.g., 784-dimensional Fashion MNIST embeddings) into 3D clusters for intuitive analysis of representation structures
Augmenting LLMs with curated and formatted data sets (RAG) directs model outputs toward desired outcomes using specific organizational or personal information.
Using high-dimensional vectors to represent tokens, words, documents, or images enables LLMs to traverse and search data sets efficiently via a vector store.
A straightforward vectorized RAG pipeline can solve complex agent problems far more effectively than heavily engineered custom solutions by leveraging indexed embeddings and retrieval.
Using bespoke fine-tuned language models on specific coding tasks like autocomplete can significantly enhance developer workflows by delivering higher-quality, context-aware code suggestions.
Evaluate both gross CAC payback for new customer acquisition and net revenue retention (expansion revenue) from existing accounts to fully assess SaaS business health.
Calculate CAC payback by summing all sales and marketing costs to acquire customers and dividing by average customer lifetime value (derived from churn rate) to measure the months until acquisition costs are recouped.
Use a high-quality evaluation set to iteratively optimize prompts and user workflows rather than investing in full model fine-tuning for most applications, cutting costs and complexity.
Embed tool-calling logic directly into the LLM as ‘intellectual grunt’ so the model natively understands and invokes developer-built tools without external orchestration.
Generate synthetic tool-use data from real developer examples, evaluate with a rubric LLM, and apply reinforcement learning to optimize the model’s tool-calling performance.
Using a sparse mixture-of-experts attention architecture activates only 32 B parameters at inference, enabling scaling to a trillion-parameter model cost-effectively.
Tom Spencer emphasized that iterative testing and real-time feedback were essential to refining MediAgent’s multi-agent medical diagnosis capabilities.
