Different LLMs have unique strengths and knowledge gaps. Instead of relying on one model, an "LLM Council" approach queries multiple models (e.g., Claude, Gemini) for the same prompt and then uses an agent to aggregate and synthesize the responses into one superior output.
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An effective AI development workflow involves treating models as a team of specialists. Use Claude as the reliable 'workhorse' for building an application from the ground up, while leveraging models like Gemini or GPT-4 as 'advisory models' for creative input and alternative problem-solving perspectives.
Instead of switching between ChatGPT, Claude, and others, a multi-agent workflow lets users prompt once to receive and compare outputs from several LLMs simultaneously. This consolidates the AI user experience, saving time and eliminating 'LLM ping pong' to find the best response.
Anthropic's new "Agent Teams" feature moves beyond the single-agent paradigm by enabling users to deploy multiple AIs that work in parallel, share findings, and challenge each other. This represents a new way of working with AI, focusing on the orchestration and coordination of AI teams rather than just prompting a single model.
Relying on a single model family for generation and review is suboptimal. Blitzy found that using models from different developers (e.g., OpenAI, Anthropic) to check each other's work produces tremendously better results, as each family has distinct strengths and reasoning patterns.
Rather than relying on a single LLM, LexisNexis employs a "planning agent" that decomposes a complex legal query into sub-tasks. It then assigns each task (e.g., deep research, document drafting) to the specific LLM best suited for it, demonstrating a sophisticated, model-agnostic approach for enterprise AI.
Generating truly novel and valid scientific hypotheses requires a specialized, multi-stage AI process. This involves using a reasoning model for idea generation, a literature-grounded model for validation, and a third system for checking originality against existing research. This layered approach overcomes the limitations of a single, general-purpose LLM.
AI expert Andrej Karpathy suggests treating LLMs as simulators, not entities. Instead of asking, "What do you think?", ask, "What would a group of [relevant experts] say?". This elicits a wider range of simulated perspectives and avoids the biases inherent in forcing the LLM to adopt a single, artificial persona.
Don't rely on a single AI model for all tasks. A more effective approach is to specialize. Use Claude for its superior persuasive writing, Gemini for its powerful analysis and image capabilities, and ChatGPT for simple, quick-turnaround tasks like brainstorming ideas.
Top performers won't rely on a single AI platform. Instead, they will act as a conductor, directing various specialized AI agents (like Claude, Gemini, ChatGPT) to perform specific tasks. This requires understanding the strengths of different tools and combining their outputs for maximum productivity.
Powerful AI tools are becoming aggregators like Manus, which intelligently select the best underlying model for a specific task—research, data visualization, or coding. This multi-model approach enables a seamless workflow within a single thread, outperforming systems reliant on one general-purpose model.
