In a November 2024 episode of the Chain of Thought podcast, Cohere’s Sara Hooker and Craig Wiley offered GenAI forecasts for 2025 that now read less like speculative fiction and more like a technical specification for modern builders. Conor Bronsdon’s recent analysis on DEV.to revisits these conversations, arguing that their insights on RAG maturity, tool orchestration, and model sizing provide a practical playbook for teams currently struggling to move AI prototypes into production. The core takeaway is that successful systems are not defined by flashy demos, but by rigorous evaluation infrastructure and narrow, purpose-built tooling.

The End of One-Click RAG Illusions

Craig Wiley identified late 2024 as the moment RAG hype collided with production reality, noting that vendor demos promising easy integration often failed at scale. His warning that 'prompt plus some docs doesn't equal a system that I can bet my company's reputation on' remains a critical benchmark for any team building retrieval systems. The article suggests treating every retrieval demo as 'phase zero,' requiring builders to explicitly define the cost of errors in customer trust or compliance before committing to an architecture. If the stakes are high, the source material insists on budgeting for compound architectures and LLM judges from day one, rather than retrofitting evaluation after an executive demo fails.

Toolification as a Maturity Metric

Wiley’s discussion on 'toolification' highlights function calling as a primary indicator of engineering maturity, advocating for narrow, orchestrated capabilities over monolithic chatbots. The analysis points out that broad-scope bots quickly hit bottlenecks in evaluation schemas and domain understanding, whereas tractable slices—such as separating order processing from troubleshooting—allow for measurable wins. The recommended decision rule is strict: if you cannot name a golden dataset for a single narrow intent, you are not ready to orchestrate multiple tools. This approach shifts the focus from general intelligence to specific, verifiable outputs, ensuring that each tool-backed flow is measured before expanding scope.

Strategic Model Sizing and Data Optimization

Sara Hooker’s dual prediction that models would simultaneously grow larger for multimodal tasks and smaller through optimization offers a clear framework for model selection. The article advises that multimodal capabilities should be reserved for tasks where modality is essential, such as parsing insurance claims or manuals with complex charts, citing Wiley’s note that multimodal costs remain prohibitive for default use. For other applications, the playbook emphasizes smaller, optimized models paired with stratified data strategies. It warns against treating synthetic data volume as a proxy for quality, urging builders to validate generated examples against real-world data to fill rare-path gaps without introducing noise.

Preference Alignment as a Measurable Objective

Hooker emphasized that safety and preference alignment are multiple objectives, requiring global norms alongside local harms. She stressed the importance of adapting models per jurisdiction, acknowledging that a single reward model cannot capture every user preference. The article connects this to the practical limits of RLHF, noting that human raters often disagree and policies can game oversight. Builders are advised to map each production endpoint to a short list of explicit objectives and refuse to widen scope until those scores are stable on a golden dataset. This treats preference alignment not as a vague 'trust' metric, but as a rigorous engineering constraint similar to latency or accuracy.

Key Takeaways

  • Treat retrieval demos as phase zero; budget for compound architecture and eval infrastructure if reputation is at stake.
  • Use function calling for narrow intents; do not orchestrate multiple tools without a golden dataset for each.
  • Reserve multimodal models for tasks where modality changes the outcome; otherwise, optimize smaller models with stratified data.
  • Define safety and preference alignment as measurable objectives using RLHF insights, refusing to widen scope until scores stabilize.

The Bottom Line

The era of relying on general-purpose LLMs for specific business tasks is ending; builders must now treat AI integration as a disciplined engineering problem where narrow scope, rigorous evaluation, and strategic model sizing determine success. Stop chasing general intelligence and start building specialized, measurable systems.