Moonshot’s trillion-parameter model uses a mixture-of-experts sparse attention design that activates only 32 billion parameters at once, demonstrating how sparse MoE can deliver large model capacity with reduced compute.
Leverage specialized services like Pinetone to automate data conversion pipelines into vector space with algorithmic variations tailored to each problem domain.
Vectorize and store embeddings only for narrow domain data sets (e.g., per-property JSON with 500 fields) to achieve better performance than prompt engineering.
Implement a two-step RAG pipeline by first running an embeddings-based similarity search to get a pointer, then executing a SQL or graph query to fetch the full detailed dataset.
Use a graph database like Neo4J to represent LLM memory and accelerate retrieval of contextual or social relationship data for tasks such as user preference lookup or fraud detection.
Combine vector-based semantic clustering with graph-based relationships to leverage cosine similarity and entity connections in your augmented generation pipeline.
Implement a Graph RAG approach by modeling your domain entities as nodes (nouns like people, places, items) and relationships as edges to enable semantic and relational retrieval alongside vector search.
Use a JSON-based ETL pipeline to extract only key email attributes (sender, receiver, body, organization, etc.) instead of raw text to drastically reduce data volume before embeddings and retrieval.
A systematic pipeline for vectorizing datasets involves defining the desired outcome, chunking and extracting data (structured vs unstructured), vectorizing with appropriate chunk overlaps, processing multimodal content like images, enriching with metadata, and storing in a vector store for retrieval.
A Doc ETL pipeline maps debate transcripts to emergent themes, extracts and formats those themes, deduplicates and merges them, then pushes the structured data into an analysis pipeline.
A simple RAG pipeline vectorizes JSON schema fields with metadata and uses an on-device lightweight model to search relevant fields, outperforming the complex multi-agent prompt-engineered system.
Cameron's initial approach used a hierarchical lang graph with a supervisor agent, determination sub-agent, tool invocations, and JSON schema translation to map natural language utterances to structured data fields.
Leverage embeddings of both user utterances and annotated JSON metadata to search for matching criteria and use LLM confidence thresholds to decide which fields to populate.
Use LLM chat turns to process streaming voice input and map natural language utterances directly to fields in a predefined JSON schema for inspection forms.
Leverage lightweight on-device models in the latest iOS releases running on phone inference chips to perform vector search and classification without server round trips.
