Generative AI Insights, an InfoWorld blog open to outside contributors, provides a venue for technology leaders to explore and discuss the challenges and opportunities presented by generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content.
Most organizations lack the foundational infrastructure needed to deploy AI agents effectively, with fragmented knowledge access and security concerns the biggest barriers.
Large language models and small language models will play different roles in ensuring that we deliver valuable generative AI applications at cost-effective levels.
RamaLama makes it a snap to spin up AI models locally in containers and streamlines the path from experimentation to production.
The governance journeys of SaaS and Web2 tell us that today’s ad hoc AI governance will give way to a continuous and automated approach.
A glimpse at how DeepSeek achieved its V3 and R1 breakthroughs, and how organizations can take advantage of model innovations when they emerge so quickly.
Building generative user interfaces into your applications can be an effective way to deliver better user experiences. It means orchestrating fully interactive responses.
Deep neural networks have hit a wall. An entirely new, backpropagation-free AI stack promises to be orders of magnitude more performant.
To unlock the full potential of AI and machine learning, understand the keys to model selection, optimization, monitoring, scaling, and metrics for success.
Hype can be excessive and obnoxious and lead to waste and false promises. It is also a crucial catalyst of experimentation and innovation.
With AI observability, we can guard against hallucinations, catch irrelevant and incomplete responses, and identify security lapses in generative AI applications — ensuring they meet the needs of the business.
How event-driven design can overcome the challenges of coordinating multiple AI agents to create scalable and efficient reasoning systems.
Why relying on retrieval-augmented generation and prompt engineering is preferable to investing in model training and fine-tuning.
While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge graph.
Before deploying agentic AI, enterprises should be prepared to address several issues that could impact the trustworthiness and security of the system.
AI red teaming offers an innovative, proactive method for strengthening AI while mitigating potential risks, helping organizations avoid costly AI incidents. Here’s how it works.
Developers are tired of hearing about AI as a panacea. The backlash may be just what organizations need to effectively implement the technology.
Small language models shine for domain-specific or specialized use cases, while making it easier for enterprises to balance performance, cost, and security concerns.
1.7 million AI chats in 30 days... and seven big lessons Campfire learned about building better AI chat products.
What’s the best way to store, search, and analyze content not based on their technical characteristics but on their meaning?
How Gencore AI enables the construction of production-ready generative AI pipelines using any data system, vector database, AI model, and prompt endpoint.
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