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Merge pull request #1960 from qdrant/essentials-course-cleanup

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Essentials course cleanup
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kanungle
2025-10-23 09:13:19 -07:00
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parent dbd2b3be4e 6f7f6fd55e
commit 26dde1e495
14 changed files with 195 additions and 149 deletions
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qdrant-landing/content/course/essentials/day-6/congratulations.md
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@@ -15,7 +15,7 @@ You've built and shipped a complete vector search application and gained the exp
You've progressed from vector search fundamentals to production-ready expertise:
**Foundation Building** (Days 0-2): You mastered the core concepts of vector search, learned how similarity metrics work, and understood how [HNSW](https://qdrant.tech/articles/filtrable-hnsw/) indexing enables fast retrieval at scale.
**Foundation Building** (Days 0-2): You mastered the core concepts of vector search, learned how similarity metrics work, and understood how HNSW indexing enables fast retrieval at scale.
**Advanced Retrieval** (Days 3-5): You implemented hybrid search combining semantic and keyword signals, explored quantization for performance optimization, and mastered the Universal Query API with multivector reranking.
@@ -40,13 +40,13 @@ Your final project demonstrates several production-critical capabilities:
{{< course-card
title="Earn your Qdrant Essentials Certificate"
image="/icons/outline/training-white.svg"
link="/course/certification/" >}}
link="/course/essentials/certification/" >}}
Get recognized for completing Day 0–6 and the final project. Add it to your LinkedIn and portfolio.
{{< /course-card >}}
## What's Next?
**Explore Advanced Integrations**: Check out [Day 9 Partner Integrations](../../day-9/) to see how Qdrant works with leading AI frameworks and data platforms.
**Explore Advanced Integrations**: Check out [Day 7 Partner Integrations](../../day-7/) to see how Qdrant works with leading AI frameworks and data platforms.
**Join the Community**: Share your final project results and connect with other practitioners building vector search systems. The Qdrant community is always excited to see what people build.
qdrant-landing/content/course/essentials/day-6/final-project.md
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@@ -144,7 +144,7 @@ Transform raw results into user-friendly output: page title, section title, URLs
### Step 7: Analyze Your Results
Build a small eval set and measure quality and latency. Use results to guide tuning (fusion strategy, candidate sizes, search-time `ef`, etc.).
Build a small eval set and measure quality and latency. Use results to guide tuning (fusion strategy, candidate sizes, search-time `hnsw_ef`, etc.).
**Ground Truth**:
- Create 20–30 realistic queries with expected section URLs/anchors.
@@ -199,7 +199,7 @@ As you test your search engine, consider:
* **Rerank or not:** Are multivectors worth it, or is fusion alone enough?
* **Performance tuning:** Which search and HNSW settings hit your accuracy/latency goals?
* Search time: raise `ef` from 64 → 128 → 256 until gains flatten.
* Search time: raise `hnsw_ef` from 64 → 128 → 256 until gains flatten.
* Index time (if rebuilding): try higher `m` (16, 32) and `ef_construct` (200, 400).
### Step 2: Post Your Results
@@ -228,7 +228,7 @@ Show your run and learn from others. **Post your results in** <a href="https://d
- **Payload fields:** <page_title, section_title, section_url, breadcrumbs, tags, prev/next>
- **Fusion:** <RRF/DBSF>, k_dense=<100>, k_sparse=<100>
- **Reranker:** ColBERT (MaxSim), top-k=<N>
- **Index/Search params:** ef=<...>, m=<...>, ef_construct=<...> # if tuned
- **Index/Search params:** hnsw_ef=<...>, m=<...>, ef_construct=<...> # if tuned
**Queries (examples)**
1) "<user query>"