> ## Documentation Index
> Fetch the complete documentation index at: https://exa.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Search Best Practices
> Tune retrieval quality, latency, context, and synthesis for production Search API integrations.
This guide assumes you already have a working [Search API request](/docs/search/quickstart). It covers how to improve that request following Exa's recommended best practices.
## Start with the smallest useful request
The best baseline is a natural-language query with `highlights`:
```python Python theme={null}
result = exa.search(
"Recent technical articles comparing hybrid and semantic retrieval for RAG systems",
contents={"highlights": True},
)
```
```javascript JavaScript theme={null}
const result = await exa.search(
"Recent technical articles comparing hybrid and semantic retrieval for RAG systems",
{ contents: { highlights: true } }
);
```
```bash cURL theme={null}
curl -s -X POST "https://api.exa.ai/search" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $EXA_API_KEY" \
-d '{
"query": "Recent technical articles comparing hybrid and semantic retrieval for RAG systems",
"contents": { "highlights": true }
}'
```
This gives you ranked pages and token-efficient context per page, relevant to the query.
Add additional parameters only as needed:
| Parameter | Add it when |
| ---------------------- | -------------------------------------------------------------------- |
| `type` | Adjusting to meet a latency budget or depth requirement |
| `numResults` | Fewer pages for a smaller context window, or more for broader recall |
| `outputSchema` | Synthesizing results or structuring them into JSON |
| `maxAgeHours` | Cached page content may be too old |
| Domain or date filters | Results outside the constraint would be unusable |
## Search vs. Deep Search
Standard search retrieves and ranks pages for a query. Deep Search runs a research process that can
search iteratively, inspect what it found, refine the search, and synthesize a grounded result.
| Need | Start with |
| ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------ |
| Ranked pages for a well-formed query | `auto` or `fast` |
| Difficult searches, synthesis across many results, or structured outputs that cannot be filled with a single search (3+ fields) | `deep` |
| Long-running research, list building, or multi-hop enrichment | [Exa Agent](/docs/agent/quickstart) |
Deep modes are recommended by default when using `outputSchema`. Read the [Deep Search guide](/docs/search/deep-search) for full instructions and examples.
## Improve retrieval quality
When results need improvement, change one part of the request at a time.
Describe the pages you want, not a bag of keywords. Include the subject and any source type,
time period, or other detail that changes what a relevant result looks like.
```text theme={null}
Benchmark papers evaluating long-context retrieval methods on legal documents
```
Look at the titles, URLs, publication dates, and highlights before changing the request.
```json theme={null}
{
"results": [
{
"title": "Long-Context Retrieval Methods on Legal Documents",
"url": "https://arxiv.org/abs/2608.00000",
"publishedDate": "2026-08-26T00:00:00.000Z",
"highlights": [
"We compare long-context retrieval methods across legal document benchmarks..."
]
}
]
}
```
The title and URL show the kind of source Exa retrieved, `publishedDate` shows
its recency, and the highlight shows the evidence that matched the query. Refine the query to
retrieve different pages, add date filters to narrow the time period, or fetch full text when
you need more context from a useful result.
Use `includeDomains`, `excludeDomains`, and publication-date filters only when a result that
violates the constraint cannot be used. Put retrieval preferences in the query and, when
synthesizing, put response instructions in `systemPrompt`.
Use a faster mode for a latency requirement or a deep mode when the retrieval process itself
needs iteration and reasoning. A different mode cannot repair an underspecified query.
Keep a small set of representative queries while tuning. Compare result relevance and downstream task success across the set instead of optimizing for one example. Record `requestId`, `searchTime`, and `costDollars` so regressions are reproducible.
## Budget latency and context
Each control spends a different resource:
| Control | What it adds |
| ------------------------- | ------------------------------------------------- |
| More results | More pages, response data, and downstream context |
| Full text | Broader page context and a larger payload |
| `summary` | One additional language model call per result |
| `outputSchema` | Synthesis across the retrieved results |
| `contents.maxAgeHours: 0` | A fresh page fetch instead of cached content |
| Deep search types | Iterative search, synthesis, and reasoning |
For a real-time path where cached content is acceptable, combine the lowest-latency mode with highlights and cache-only content:
```python Python theme={null}
result = exa.search(
"Recent product updates from major AI labs",
type="instant",
contents={
"highlights": True,
"max_age_hours": -1,
},
)
```
```javascript JavaScript theme={null}
const result = await exa.search("Recent product updates from major AI labs", {
type: "instant",
contents: {
highlights: true,
maxAgeHours: -1
}
});
```
```bash cURL theme={null}
curl -s -X POST "https://api.exa.ai/search" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $EXA_API_KEY" \
-d '{
"query": "Recent product updates from major AI labs",
"type": "instant",
"contents": {
"highlights": true,
"maxAgeHours": -1
}
}'
```
Do not use this recipe when page freshness is part of correctness. Start from `auto` and default freshness unless the product has a measured latency target.
**Allocate context across results with Dynamic Highlights**: Use `contents.highlights.dynamic` when several pages will feed the same agent or RAG context. Exa spends more context on useful results and less on redundant ones.
```json theme={null}
{
"query": "What is the current Fed interest rate?",
"contents": {
"highlights": {
"dynamic": true
}
}
}
```
Keep regular highlights when every result needs its own excerpt or a per-page character limit. Requests that set `dynamic: true` require the `Exa-Beta: dynamic-highlights-2026-08-28` header. See [Dynamic highlights](/docs/search/highlights#dynamic-highlights) for response behavior and limitations.
## Tips for common use cases
| If you need | Use | Avoid |
| ------------------------------------ | ---------------------------------------------------------------- | ------------------------------------------------- |
| Newer publications | Put the time window in the query or use publication-date filters | `maxAgeHours` |
| Fresh content from pages that change | `contents.maxAgeHours` | Publication-date filters |
| A preferred kind of source | Query phrasing; `systemPrompt` when synthesizing | A hard domain allowlist |
| Results only from approved sources | `includeDomains` | Repeating `site:` in the query |
| A small structured output | `outputSchema` with standard Search | Choosing Deep only because the output is JSON |
| A multi-item researched output | `deep` with `outputSchema`, or [Exa Agent](/docs/agent/quickstart) | Expecting one retrieval pass to gather every item |
| More context from a few pages | Search with highlights, then call Contents | Full text for every result |
| Lower latency | Measure `fast` or `instant` with compact content | Adding freshness or synthesis controls by default |
## When to use another endpoint
Use a different Exa endpoint when the task changes shape:
| Task | Use |
| --------------------------------------------------- | -------------------------------- |
| Long-running research, list building, or enrichment | [Exa Agent](/docs/agent/quickstart) |
| The URLs are already known | [Contents](/docs/contents/quickstart) |
| Run a search on a schedule | [Monitors](/docs/monitors/quickstart) |
## Next steps
Every request parameter and response field.
Core request shapes, filters, output, and freshness.
Extract highlights or full text from pages you already know.
Long-running research, list building, and enrichment.