Two-Step Contextual Enrichment, or TSCE, is an innovative framework designed to enhance the accuracy and reliability of large language models (LLMs) and AI agents. This model-agnostic approach enables a significant increase in performance, with noted improvements ranging from 20 to 30 percentage points. The framework is particularly valuable for developers aiming to achieve higher accuracy in their AI applications.
The TSCE framework operates by implementing a two-phase process that enriches the context provided to LLMs, allowing them to produce more precise answers. By running over 4,000 test prompts, the framework has demonstrated its capability to uplift model performance consistently. This makes it an attractive option for organizations seeking to optimize their AI systems without being tied to a specific model.
This open-source project not only contributes to the development of reliable AI solutions but also encourages collaboration within the community. Developers can access the framework and its resources to further explore its capabilities and benefits. For those interested in enhancing their AI systems, TSCE offers a promising avenue toward improved accuracy and reproducibility.
You can learn more by visiting TSCE Demo.