> ## Documentation Index > Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt > Use this file to discover all available pages before exploring further. # XO GPT Response Rephrasing Model [Back to XO GPT Model Specifications](/ai-for-service/generative-ai-tools/xo-gpt-module#xo-gpt-model-specifications) The XO GPT Response Rephrasing model enhances the quality and naturalness of AI Agent interactions. It rephrases predefined responses to preserve conversation context, adapt to user emotion, and produce more empathetic, human-like replies—improving user satisfaction without changing the underlying content. *** ## Challenges with Commercial Models | Challenge | Impact | | -------------------------- | ----------------------------------------------------------------------------------------------- | | **Latency** | High processing times affect user experience in real-time or high-volume scenarios. | | **Cost** | Per-request pricing scales poorly for large deployments. | | **Data Governance** | Sending conversations to external models raises privacy and security concerns. | | **Lack of Customization** | General-purpose models are not tuned for specific industries or use cases. | | **Limited Control** | Minimal ability to correct or refine model behavior for incorrect outputs. | | **Compliance Constraints** | Some industries have regulatory requirements that commercial LLM providers don't fully support. | *** ## Key Assumptions * Designed for text-based conversations only. * Rephrases AI Agent responses only; does not modify user inputs. *** ## Benefits XO GPT Benefits ### Empathetic and Contextual Communication Tailors responses to user sentiment and tone for engaging, empathetic interactions. See [Model Benchmarks](#model-benchmarks) for performance metrics. ### Cost-Effective For Enterprise Tier customers, XO GPT eliminates commercial model usage costs. Example comparison (120 input tokens/conversation, 10,000 daily interactions, 40 tokens/response): | Model | Input \$/MTok | Output \$/MTok | Input \$/Year | Output \$/Year | Total \$/Year | | ----------- | ------------- | -------------- | ------------- | -------------- | ------------- | | GPT-4 Turbo | \$30 | \$60 | \$13,140 | \$8,760 | \$21,900 | | GPT-4 | \$10 | \$30 | \$4,380 | \$4,380 | \$8,760 | | GPT-4o Mini | \$0.15 | \$0.60 | \$65.70 | \$87.60 | \$153.30 | ### Enhanced Security No client or user data is used for model retraining. **Guardrails:** Content moderation, behavioral guidelines, response oversight, input validation, and usage controls. **AI Safety:** Ethical guidelines, bias monitoring, transparency, and continuous improvement. Performance, features, and language support may vary by implementation. Test thoroughly in your environment before production use. *** ## Use Cases | Domain | Use Cases | | -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | | Customer Support | Rephrasing responses to sound empathetic during complaints; converting technical jargon to plain language; rephrasing policy updates reassuringly | | Healthcare | Rephrasing medical instructions for clarity and compassion; responding to sensitive queries with emotional sensitivity | | Banking & Finance | Rephrasing declined transaction responses apologetically; simplifying financial product explanations; communicating loan rejections with empathy | | Education | Rephrasing student feedback constructively; simplifying academic instructions | | Human Resources | Rephrasing interview feedback constructively; communicating policy updates empathetically | | Legal | Rephrasing legal notices for clarity; simplifying contractual terms without losing accuracy | | E-commerce | Rephrasing out-of-stock responses to maintain trust; communicating refund/return policies reassuringly | | Social Media | Rephrasing user content for brand alignment; crafting empathetic responses to negative feedback | | IT Support | Rephrasing troubleshooting steps for clarity; handling escalations professionally | | Travel & Hospitality | Rephrasing cancellation responses to minimize dissatisfaction; simplifying travel updates | *** ## Sample Output **Conversation:** ``` Current intent: book a flight User: I need to book a flight to New York, but I'm really anxious about traveling amidst the pandemic. AI Agent: Can I know your preferred date of travel? User: I'm planning to fly on the 3rd of next month. AI Agent: Could you please tell me your city of departure? User: I will be departing from Los Angeles. AI Agent: What class of service do you prefer for your flight? User: I prefer economy class, but I'm concerned. Are the flights safe? AI Agent: Could you please let me know your preferred time of flight? ``` **Rephrased response:** > Absolutely, your worries are entirely understandable. However, please be assured that airlines are practicing stringent safety measures to ensure the well-being of all passengers. Now, could you please share your preferred flight time? *** ## Model Building Process See [Model Building Process](/ai-for-service/generative-ai-tools/xogpt-model-specifications#model-building-process). *** ## Model Benchmarks | Version | Accuracy | TPS | Latency (s) | Benchmark | Test Data | | ------- | -------- | --- | ----------- | ------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | v1.0 | 99% | 45 | 0.9 | [Summary v1](#benchmarks-summary-v1) | [Results v1](https://github.com/Koredotcom/docs-v2/raw/refs/heads/main/ai-for-service/generative-ai-tools/test-date-and-results/xogpt-response-rephrasing-v1.0.xlsx) | *** ## Version 1.0 ### Model Choice Base model: [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | Base Model | Developer | Language | Release Date | Status | Knowledge Cutoff | | ------------------------ | ---------- | ------------- | ------------ | ------ | ---------------- | | Mistral 7B Instruct v0.2 | Mistral AI | Multi-lingual | March 2024 | Static | September 2024 | ### Fine-Tuning Parameters | Parameter | Description | Value | | ----------------------- | ----------------------------------------- | --------------------- | | Load in 4-bit Precision | Reduce memory by loading weights at 4-bit | True | | Use Double Quantization | Improve accuracy with double quantization | True | | 4-bit Quantization Type | Type of 4-bit quantization | nf4 | | Computation Data Type | Data type for 4-bit quantized weights | torch.float16 | | LoRA Rank | Rank of low-rank decomposition | 32 | | LoRA Alpha | LoRA scaling factor | 16 | | LoRA Dropout Rate | Dropout to prevent overfitting | 0.05 | | Bias Term Inclusion | Add bias terms in LoRA layers | — | | Task Type | LoRA task type | CAUSAL\_LM | | Targeted Modules | Layers where LoRA is applied | `["query_key_value"]` | ### General Parameters Infrastructure: A10 (g5-xlarge). | Parameter | Description | Value | | ------------------- | -------------------------- | ------------------ | | Learning Rate | Rate toward loss minimum | 2e-4 (0.0002) | | Batch Size | Examples per training step | 2 | | Epochs | Passes over training data | 4 | | Max Sequence Length | Maximum input length | 32k | | Optimizer | Optimization algorithm | paged\_adamw\_8bit | ### Benchmarks Summary v1 Comparison models: Mistral-vanilla, GPT-4. Benchmarks Summary v1 See [Test Data and Results v1](https://github.com/Koredotcom/docs-v2/raw/refs/heads/main/ai-for-service/generative-ai-tools/test-date-and-results/xogpt-response-rephrasing-v1.0.xlsx) for full details.