ChatGPT Development Services and Integration

We add guardrails before and after the LLM call.

The architecture can include input classification, prompt injection detection, retrieved-context validation, output checks, allowlisted tools, and audit logs. If a request tries to override system instructions or access restricted data, the middleware can block it before it reaches the core workflow.

No. The right architecture depends on your compliance needs, provider terms, and deployment requirements.

Options can include Azure OpenAI with enterprise data controls, OpenAI API with configured retention settings, local PII redaction before the LLM call, or self-hosted open-source models deployed inside your infrastructure.

Large context windows do not remove the need for retrieval. Sending long prompts is expensive and can degrade answer quality when relevant information is buried deep within a long input. A 2023 study of long-context models, “Lost in the Middle” by Liu et al., found that performance can drop when the relevant information appears in the middle of the context. RAG reduces the prompt to the most relevant passages and helps control cost.

We can design the application with an LLM routing layer.

If the primary model endpoint fails, times out, or reaches a rate limit, the application can route selected requests to another approved model. The fallback behavior depends on the use case, response requirements, compliance rules, and available providers.

Most enterprise knowledge assistant use cases start with RAG. RAG connects the model to current company data without retraining it whenever a document changes. Fine-tuning is more useful when the model needs to follow a narrow output style, domain-specific syntax, coding patterns, or a regulated document format. When it is, our LLM fine-tuning work covers it.

Yes, but the data pipeline matters.

Before integrating the LLM, we can build ETL pipelines that extract text through OCR, parse documents, split content into meaningful chunks, add metadata, and index the cleaned content in a vector database. The assistant then retrieves prepared knowledge instead of guessing from raw files.

We replace simple pass/fail testing with evaluation pipelines.

Before deployment, we test prompts against expected behavior and score outputs for retrieval quality, context use, faithfulness, formatting, and unsafe content. If a new build fails to meet the accepted threshold, the deployment can be blocked or sent for review.