Text2SQL caching becomes practical only when it caches not the answers, but the structure of the query. In this architecture, SQL templates reduce latency, token cost, and load on the LLM.
The problem manifested where many AI prototypes begin to fail in production. User queries were processed in 25–30 seconds, and at this stage, engagement dropped. For Text2SQL, this is critical: the system can be accurate, but too slow to appear as a working tool. Without caching, each question triggered a call to the LLM to generate SQL, which meant unpredictable response times, throttling limits, and increased token costs along with traffic.
We did not opt for replacing the model with a smaller and faster one. The original text explicitly states that attempts to switch to smaller models degraded quality, and the degradation of accuracy was unacceptable. Therefore, the choice was a compromise, but technically sound: maintain generation quality and eliminate unnecessary repeated calls in that part of the pipeline where queries differ not in meaning, but in parameters. Thus, the idea emerged to cache not the user question and not the prepared answer, but the SQL query and its template.
This is an important architectural shift. The cache of the answer quickly becomes outdated because data in operational databases changes constantly. The cache of the SQL query lasts longer because a query like `SELECT SUM(revenue) FROM sales WHERE quarter = ‘Q3’` retains its meaning even if the underlying data has already been updated. Furthermore, the team noticed a stronger pattern: many queries differed only by filters. Hence the templating, where `quarter=’Q3’` turns into `{quarter}`. This approach increases reuse without losing data relevance.
The key challenge was not in generating templates, but in finding them. A user might ask, “Show me Q3 sales,” or they might write, “What were sales in Q3?”. String matching is useless here. Therefore, each template pair is stored along with the vector embedding of the original question, and a new query is compared through semantic similarity search. If the confidence threshold is high enough, the system extracts entities, fills in placeholders, and goes directly to executing SQL. This bypasses the costly LLM call for query generation.
The implementation here is structured as a pipeline with clear branches. First comes entity recognition, and the system considers not only the current question but also conversation history, current date, and user preferences. This helps to interpret references like “last month” or “my region.” Then the embedding of the new question searches for the closest template in the cache. If a match is found, the placeholders are filled, and SQL is executed as a parameterized database query, rather than through string interpolation. This simultaneously reduces the risk of SQL injection and helps filter out errors in entity extraction.
If the template is not found, the system reverts to the standard Text2SQL pipeline. There, the LLM generates SQL with the full context of the schema and examples. After a successful fallback query, the system does not discard the result. It attempts to generalize the query back into a template and add it to the cache. This creates a self-improving loop: coverage grows as real user queries reveal new patterns. Such a mechanism is particularly useful in domains where the set of formulations is limited, and the structure of questions repeats.
An important nuance is the verification of result sufficiency. After executing SQL, the system sends the results to the response generation model, which must not only formulate a response but also determine whether the data is sufficient for it. If the query asked for “Q3 sales by region,” and the template returned only the overall total, the response is considered incomplete. This reduces the risk of confidently presenting a partial result as complete. For a production system, this is not a cosmetic detail, but a protective layer against silent errors.
The result is described without attempting to turn it into a marketing metric. In production deployment, parameterized SQL templates reduced end-to-end latency by 80% and decreased token consumption by more than 50%. At the same time, the authors clarify that specific figures depend on schema size, prompt design, model choice, and query mix. Thus, the main takeaway is not in absolute values, but in the direction of improvement. The most expensive stage of Text2SQL—generating SQL through the LLM—was moved from the hot path to cache hits, which provided the main gain.
In engineering terms, this is a pragmatic pattern. The system does not attempt to “speed up the LLM” as a black box. It reduces the number of calls to it where the task can already be structurally solved. For Text2SQL, this is particularly logical: the meaning of the query often repeats, only the values change. This is why template caching turns out not to be a workaround, but a more precise level of abstraction.