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RAG fails to fix hallucination; new mid-tier model targets agent costs

By PulseAugur Editorial · [2 sources] ·

Retrieval-augmented generation (RAG) has been criticized for not solving AI hallucination, instead shifting the problem to the retrieval stage. A new mid-tier model, priced at $2/M, has achieved 63.2% on the SWE-bench Pro benchmark, indicating a potential shift in the cost and accessibility of AI agents. AI

IMPACT RAG's limitations highlight the need for better fact-checking in AI systems, while new pricing models may influence the cost-effectiveness of AI agents.

RANK_REASON The cluster discusses limitations of RAG and pricing strategies for AI models, which falls under commentary on AI development and market dynamics.

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

RAG fails to fix hallucination; new mid-tier model targets agent costs

COVERAGE [2]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    RAG didn't fix hallucination. It moved it upstream: now the model faithfully reproduces whatever your retriever surfaced, garbage included. You didn't add a fac

    RAG didn't fix hallucination. It moved it upstream: now the model faithfully reproduces whatever your retriever surfaced, garbage included. You didn't add a fact-checker, you added a very confident librarian with bad shelving. # AI # MachineLearning # LLM # Threadverse # Tech

  2. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    63.2% on SWE-bench Pro, cheaper than the flagship, $2/M intro pricing. Flagships are PR; the mid-tier sets what agents cost to run at scale — and intro pricing

    63.2% on SWE-bench Pro, cheaper than the flagship, $2/M intro pricing. Flagships are PR; the mid-tier sets what agents cost to run at scale — and intro pricing is a land grab for those workloads. Watch the tier below the headline. # AI # MachineLearning # LLM # Threadverse # Tech

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