GPT-4's natural language output still won't be reliably usable as input to downstream programs such as databases or virtual assistants.

A prediction about GPT-4 once released (March 2023); judge it at roughly one year after release (2024-03). True: evidence shows GPT-4's natural-language output could not reliably be passed as input to downstream programs without errors, e.g., documented malformed outputs, hallucinated fields or inconsistent formats when connected to databases, APIs, or assistants, such that developers needed validation, retries, or added structured-output tooling to cope. Mostly true: unreliability is documented but substantially mitigated in practice by function calling or JSON mode within that year, though not eliminated. Mixed: the evidence is split, reliable for some downstream uses but not others. Mostly false: GPT-4 output was broadly reported as reliably usable downstream, with only isolated failures. False: it was shown to be reliably usable as downstream input without notable failures.

Set before the research began.

1. E1 Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs arXiv · 31 January 2026

   “Furthermore, even the most effective text-to-SQL models, i.e. GPT-4, only achieve 54.89% in execution accuracy, which is still far from the human result of 92.96%, proving that challenges still stand.”

2. E2 Introducing Structured Outputs in the API OpenAI · 6 August 2024

   “Developers have long been working around the limitations of LLMs in this area via open source tooling, prompting, and retrying requests repeatedly to ensure that model outputs match the formats needed to interoperate with their systems.”

3. E3 A simple Claude Opus v. GPT-4 structured JSON benchmark 96layers.ai · 10 June 2024

   “The overall performance was good. Using Instruction Set 3 as a baseline — which included the full set of instructions and reminders but without any JSON errors added to the template — Claude Opus scored 98% while GPT-4 scored 99.6%.”

4. E4 Function calling and other API updates OpenAI · 13 June 2023

   “Function calling allows developers to more reliably get structured data back from the model.”

E1 reports GPT-4 reaching only 54.89% execution accuracy when turning natural-language questions into SQL for databases, against a human result of 92.96%. E2 (OpenAI, August 2024) says developers had long relied on tooling, prompting and repeated retries to get outputs in the formats their systems needed. E4 shows OpenAI added function calling in June 2023 so developers could get structured data back "more reliably", and E3 reports GPT-4 at 99.6% on a structured JSON benchmark's baseline instruction set, though that benchmark dates from June 2024, after the judging window. Taken together, the unreliability is documented [E1, E2], and within the year OpenAI introduced function calling to make structured output more reliable [E4], while E2 shows workarounds were still in use; the report treats this as the criteria's "mostly true".