LLMs are powerful for understanding user input and generating human‑like text, but they are not reliable arbiters of logic. A production‑grade system should:
- Isolate the LLM to language tasks only.
- Put all business rules and tool orchestration in deterministic code.
- Validate every step with automated tests and logging.
- Prefer local models for sensitive domains like healthcare.
| **Issue** | **What users observed** | **Common solutions** |
|-----------|------------------------|----------------------|
| **Hallucinations & false assumptions** | LLMs often answer without calling the required tool, e.g., claiming a doctor is unavailable when the calendar shows otherwise. | Move decision‑making out of the model. Let the code decide and use the LLM only for phrasing or clarification. |
| **Inconsistent tool usage** | Models agree to user requests, then later report the opposite (e.g., confirming an appointment but actually scheduling none). | Enforce deterministic tool calls first, then let the LLM format the result. Use “always‑call‑tool‑first” guards in the prompt. |
| **Privacy concerns** | Sending patient data to cloud APIs is risky. | Prefer self‑hosted/local models (e.g., LLaMA, Qwen) or keep all data on‑premises. |
| **Prompt brittleness** | Adding more rules can make prompts unstable; models still improvise. | Keep prompts short, give concrete examples, and test with a structured evaluation pipeline. |
| **Evaluation & monitoring** | Without systematic “evals,” failures go unnoticed. | Build automated test suites (e.g., with LangChain, LangGraph, or custom eval scripts) that verify correct tool calls and output formats. |
| **Workflow design** | Treat the LLM as a *translator* rather than a *decision engine*. | • Extract intent → produce a JSON/action spec → execute deterministic code → have the LLM produce a user‑friendly response. <br>• Cache common replies to avoid unnecessary model calls. |
| **Alternative UI** | Many suggest a simple button‑driven interface for scheduling. | Use the LLM only for natural‑language front‑end; the back‑end remains a conventional, rule‑based system. |
- Isolate the LLM to language tasks only.
- Put all business rules and tool orchestration in deterministic code.
- Validate every step with automated tests and logging.
- Prefer local models for sensitive domains like healthcare.
| **Issue** | **What users observed** | **Common solutions** |
|-----------|------------------------|----------------------|
| **Hallucinations & false assumptions** | LLMs often answer without calling the required tool, e.g., claiming a doctor is unavailable when the calendar shows otherwise. | Move decision‑making out of the model. Let the code decide and use the LLM only for phrasing or clarification. |
| **Inconsistent tool usage** | Models agree to user requests, then later report the opposite (e.g., confirming an appointment but actually scheduling none). | Enforce deterministic tool calls first, then let the LLM format the result. Use “always‑call‑tool‑first” guards in the prompt. |
| **Privacy concerns** | Sending patient data to cloud APIs is risky. | Prefer self‑hosted/local models (e.g., LLaMA, Qwen) or keep all data on‑premises. |
| **Prompt brittleness** | Adding more rules can make prompts unstable; models still improvise. | Keep prompts short, give concrete examples, and test with a structured evaluation pipeline. |
| **Evaluation & monitoring** | Without systematic “evals,” failures go unnoticed. | Build automated test suites (e.g., with LangChain, LangGraph, or custom eval scripts) that verify correct tool calls and output formats. |
| **Workflow design** | Treat the LLM as a *translator* rather than a *decision engine*. | • Extract intent → produce a JSON/action spec → execute deterministic code → have the LLM produce a user‑friendly response. <br>• Cache common replies to avoid unnecessary model calls. |
| **Alternative UI** | Many suggest a simple button‑driven interface for scheduling. | Use the LLM only for natural‑language front‑end; the back‑end remains a conventional, rule‑based system. |
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1. Composio - Seamless integration of tools with LLMs.
2. Weaviate - AI-native vector database for AI apps.
3. Haystack - Framework for building efficient RAG pipelines.
4. LitGPT - Pretrain, fine-tune, and deploy models at scale.
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7. AirByte - Reliable and extensible open-source data pipeline.
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1. Composio - Seamless integration of tools with LLMs.
2. Weaviate - AI-native vector database for AI apps.
3. Haystack - Framework for building efficient RAG pipelines.
4. LitGPT - Pretrain, fine-tune, and deploy models at scale.
5. DsPy - Framework for programming LLMs.
6. Portkey's Gateway - Reliably route to 200+ LLMs with one API.
7. AirByte - Reliable and extensible open-source data pipeline.
8. AgentOps - Agents observability and monitoring.
9. ArizeAI's Phoenix - LLM observability and evaluation.
10. vLLM - Easy, fast, and cheap LLM serving for everyone.
11. Vercel AI SDK - Easily build AI-powered products.
12. LangGraph - Build language agents as graphs.
13. Taipy - Build AI apps in Python.
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