Four real-world scenarios showing how query-aware prompt compression with SuperCompress reduces token costs by 60–82% while preserving answer quality.
These case studies are based on anonymized production data from applications using SuperCompress. Each scenario uses a different LLM provider and prompt structure, demonstrating the breadth of the compression approach.
The full prompt compression guide covers the methods in detail. The interactive playground lets you test compression on your own prompts.
A B2B SaaS company's support chatbot included the full product documentation (~8K tokens) as context for every user query. Most users asked about 2–3 features, but the chatbot loaded all documentation pages for every conversation.
SuperCompress was added as a preprocessing step between the memory assembly and the LLM call. For each user question, the compressor scored each documentation section and kept only the relevant ones.
The 15ms added latency was imperceptible to users. The support team reported no increase in escalation rates or negative feedback about response quality.
A legal tech platform's RAG pipeline retrieved the top-20 chunks (typically 6K–12K tokens) for each query. Many chunks were contextually related but irrelevant to the specific question — e.g., retrieving clauses about both termination and indemnification when the user only asked about termination.
SuperCompress was integrated after the retrieval step, scoring each chunk against the query and dropping the irrelevant ones before constructing the LLM prompt.
Vector retrieval (top-K) already filtered semantically, so the inputs were generally relevant. Even so, SuperCompress removed an additional 74% of tokens because many retrieved chunks addressed different aspects than the user's specific question.
A code review agent analyzed every changed file in a pull request, including the full file content for context. A typical PR with 10 changed files produced ~15K tokens of context. Many files were only tangentially related to the changes (import reordering, whitespace fixes, dependency bumps).
Compression was applied per-file, scoring each file's diff against the PR description and title. Files with changes unrelated to the PR purpose had their unchanged context stripped.
Code review accuracy was measured as the percentage of actual issues found that were identified by the agent. The 95.8% retention rate was achieved because the compressor preserved code patterns and security-relevant changes while dropping boilerplate modifications.
A customer analytics platform extracted structured data (issue category, severity, product area, action items) from support ticket transcripts. The average ticket was 3K–5K tokens, and the extraction required the full transcript to be sent to the LLM.
SuperCompress was applied to each ticket, keeping only the passages relevant to each extraction field. Since the extraction queries were consistent (e.g., "What product area does this issue affect?"), the compression was highly targeted.
The 60% reduction was lower than other cases because extraction tasks required more context — a ticket's resolution is often implied across multiple messages. The compressor was configured to be more conservative to preserve extraction quality.
| Scenario | Before | After | Savings | Quality |
|---|---|---|---|---|
| Customer support chatbot | $2,100/mo | $460/mo | 78% | 97.2% |
| Legal RAG pipeline | $1,800/mo | $470/mo | 74% | 98.1% |
| Code review agent | $950/mo | $170/mo | 82% | 95.8% |
| Data extraction pipeline | $1,600/mo | $640/mo | 60% | 99.1% |
| Combined | $6,450/mo | $1,740/mo | 73% | 97.6% avg |
Across all four scenarios, the average cost reduction was 73% with 97.6% average quality retention. The consistent pattern was that consumer and enterprise apps carry significantly more context in prompts than they need for any single user query — and a query-aware compressor can identify and remove the surplus.