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Showing 1081–1100 of 1502 insights
TitleEpisodePublishedCategoryDomainTool TypePreview
Graph RAG ArchitectureEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Implement a Graph RAG approach by modeling your domain entities as nodes (nouns like people, places, items) and relationships as edges to enable seman...
Structured JSON ETLEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Use a JSON-based ETL pipeline to extract only key email attributes (sender, receiver, body, organization, etc.) instead of raw text to drastically red...
Vectorization Pipeline FrameworkEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksDevops-
A systematic pipeline for vectorizing datasets involves defining the desired outcome, chunking and extracting data (structured vs unstructured), vecto...
Doc ETL debate analysisEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksDevops-
A Doc ETL pipeline maps debate transcripts to emergent themes, extracts and formats those themes, deduplicates and merges them, then pushes the struct...
ETL pipeline for unstructured dataEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksDevops-
ETL (extract, transform, load) is used to convert raw unstructured data into structured formats to ensure reliable downstream analysis.
Vectorized Schema RetrievalEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
A simple RAG pipeline vectorizes JSON schema fields with metadata and uses an on-device lightweight model to search relevant fields, outperforming the...
Multi-Level Agent ArchitectureEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksArchitecture-
Cameron's initial approach used a hierarchical lang graph with a supervisor agent, determination sub-agent, tool invocations, and JSON schema translat...
Embedding-based Field MatchingEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Leverage embeddings of both user utterances and annotated JSON metadata to search for matching criteria and use LLM confidence thresholds to decide wh...
Real-time Transcript JSON MappingEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Use LLM chat turns to process streaming voice input and map natural language utterances directly to fields in a predefined JSON schema for inspection ...
On-Device Model InferenceEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Leverage lightweight on-device models in the latest iOS releases running on phone inference chips to perform vector search and classification without ...
Vectorized JSON Schema MappingEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksArchitecture-
Use simple vectorization of a JSON schema and data dictionary to map natural language inspection input to a strict JSON output via vector search.
Embedding visualization with TensorFlowEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksArchitecture-
Leverage TensorFlow’s Embedding Projector to display high-dimensional embeddings in 2D or 3D using cosine similarity for exploratory analysis of vecto...
UMAP clustering visualizationEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksFrontend-
Use the UMAP project to reduce high-dimensional data (e.g., 784-dimensional Fashion MNIST embeddings) into 3D clusters for intuitive analysis of repre...
Retrieval-Augmented GenerationEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Augmenting LLMs with curated and formatted data sets (RAG) directs model outputs toward desired outcomes using specific organizational or personal inf...
Vectorization for LLMsEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Using high-dimensional vectors to represent tokens, words, documents, or images enables LLMs to traverse and search data sets efficiently via a vector...
RAG Architectural BasicsEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Retrieval-Augmented Generation (RAG) integrates a vector store for embeddings with LLM queries to enhance responses via relevant external data.
Simple Vectorized RAGEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
A straightforward vectorized RAG pipeline can solve complex agent problems far more effectively than heavily engineered custom solutions by leveraging...
Fine-tuned Autocomplete ModelsEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Using bespoke fine-tuned language models on specific coding tasks like autocomplete can significantly enhance developer workflows by delivering higher...
Gross vs Net Revenue ModelEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksAi-development-
Evaluate both gross CAC payback for new customer acquisition and net revenue retention (expansion revenue) from existing accounts to fully assess SaaS...
CAC Payback Calculation FrameworkEp 8 - Kimi2, Is RAG still a thing? and the coming SaaS bloodbath.7/18/2025FrameworksFrontend-
Calculate CAC payback by summing all sales and marketing costs to acquire customers and dividing by average customer lifetime value (derived from chur...
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