Unlock Cost Savings and Quality Improvements with Sturdy Statistics
The Scaling Problem With Per-Token Costs
Scaling Retrieval-Augmented Generation (RAG) apps is challenging due to the linear nature of LLM costs. With per-token charges, profitability becomes a moving target as usage grows.
Instead of compromising on model quality or inflating costs, you need an efficient technology that delivers great results at lower costs.
Discover how Sturdy Statistics can achieve an astonishing 97.5% cost reduction for RAG applications!
How Sturdy Statistics Delivers Value
- Efficient Retrieval = Lower LLM Costs
- By retrieving only the most relevant information with precision, we dramatically reduce prompt sizes and directly cut LLM usage costs. Fewer tokens mean significant savings for your operations.
- Improved Quality While Reducing Costs
- Many LLMs struggle with token overload, a phenomenon researchers call the “Lost in the Middle” problem.1 In fact, the true effective context window of many models may be much shorter than advertised.2
Efficient retrieval provides concise, relevant information that improves response quality while drastically reducing costs.
Unlock Efficiency and Scalability
Join the next wave of efficient, scalable AI-powered applications. Choose Sturdy Statistics to unlock the potential of a cost-effective RAG solution.
Notes
1 See Liu et al. (2023).
N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang. Lost in the Middle: How Language Models Use Long Contexts. arXiv e-prints, art. arXiv:2307.03172, July 2023. URL: https://arxiv.org/abs/2307.03172.
2 See NVIDIA's research on this subject.
C.-P. Hsieh, S. Sun, S. Kriman, S. Acharya, D. Rekesh, F. Jia, Y. Zhang, and B. Ginsburg. RULER: What's the Real Context Size of Your Long-Context Language Models? arXiv e-prints, art. arXiv:2404.06654, Apr. 2024. URL: https://arxiv.org/abs/2404.06654.