A simple vectorized Retrieval-Augmented Generation pipeline can outperform complex engineered agent solutions for certain AI tasks by efficiently retr...Show more

Guaranteeing that no code will be leaked is a systematic strategy when pitching AI coding tools to enterprises to address data-security concerns.

Transition SaaS pricing from per-seat licensing to a consumption-based, agentic model where customers pay for AI executions rather than user seats.

Compute CAC payback by dividing total sales and marketing costs per customer by the average customer lifetime value to estimate months to recoup acqui...Show more

Use an agent evaluation methodology by overloading an LLM with 30–40 distinct tools to observe its decision-making and tool-selection accuracy under h...Show more

Apply low-rank adaptation (LoRA) for very cheap, narrow-task fine-tuning when absolutely necessary to specialize a general model.

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 ...Show more

Train or fine-tune large models on specialized domain corpora—like medical literature—to create agents with deep, expert-level knowledge in that field...Show more

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...Show more

A pipeline gathering real developer MCP examples to generate vast synthetic tool-calling data, judged by an LLM rubric and refined via reinforcement l...Show more

Models at Google DeepMind generate their own synthetic data via reinforcement learning to extend token limits and advance capabilities without externa...Show more

Moonshot’s trillion-parameter model uses a mixture-of-experts sparse attention design that activates only 32 billion parameters at once, demonstrating...Show more

Many AI stacks are "pivoted to agents," suggesting building AI systems centered around autonomous agent frameworks.

Apply a multi-layered approach to agent memory—drawing on MEM0 and MM OS papers—to structure long-term and short-term memory in AI agents.

Leverage specialized services like Pinetone to automate data conversion pipelines into vector space with algorithmic variations tailored to each probl...Show more

Use smaller local models to extract key metadata or summaries from documents to handle tasks without requiring large-scale vector storage.

Vectorize and store embeddings only for narrow domain data sets (e.g., per-property JSON with 500 fields) to achieve better performance than prompt en...Show more

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 fetc...Show more

Use a graph database like Neo4J to represent LLM memory and accelerate retrieval of contextual or social relationship data for tasks such as user pref...Show more

Combine vector-based semantic clustering with graph-based relationships to leverage cosine similarity and entity connections in your augmented generat...Show more