Results for “context engineering”

…experiments showed promise, but model capability and short context windows kept them from becoming dependable products. A guest credits a later Sonnet model with making the previously shipped agent practical. Custom Agents took longer…

…without reporting a measured improvement. The immediate engineering practice was more concrete: evaluate retrieved context separately from the answer. Phind measured whether answers were right or wrong rather than directly measuring a…

…deciding how much checking is appropriate becomes a central engineering problem. Better behavior does not make evaluation straightforward. As models improve, their remaining faults become harder to discover, while unresolved edge cases…

…Intelligence. Retrieval, model architectures, and software engineering would all matter to LlamaIndex, though Liu qualifies the suggestion that his earlier ranking work was already RAG. LlamaIndex began with a concrete constraint: Liu…

…Its proposed advantage is the platform’s existing application context: runtime data, error logs, preview deployments, development-server setup, and configured secrets. These integrations reduce the onboarding work needed to make an agent…

…become capabilities such as generating text or images. The engineering question is what those capabilities let a customer do. Putting a model into production can mean three different operating situations: - Hosted API: call a provider’s…

…GGML quantized versions and efforts to expand how much context the model can handle. The host also defends precise terminology: practical benefits do not erase the importance of an established licensing definition. Three release patterns…

…said the model was natively trained to operate across multiple context windows through compaction. Maintaining continuity when a task exceeds the context available at once becomes partly a learned capability. The resulting engineering cycle…

…is too costly to be useful. The graph debate leads to a general engineering rule: choose the better implementation when its extra cost is marginal. Larger investments in frameworks and reusable libraries require expected-value reasoning.…

…for attribution may have little reason to share it. Applied to engineering, the response is optimistic. More human and digital engineers increase the supply of labor, but AI tools may also expand the economic value of problems engineers can…

…paying for a capable model rather than spending substantial engineering time on routing. Larger usage volumes and experienced staff can make routing more worthwhile. The discussed research projects also benefited from OpenAI credits, so the…

…multimodal models, including for browser interactions. A broad engineering stack can serve a narrow application goal: work on infrastructure, pretraining, reinforcement learning, fine-tuning, and products did not require building every…