docs: fix typos and improve clarity in quantization/turboQuant docs (#2371)

qdrant-landing/content/articles/binary-quantization.md

@@ -181,7 +181,7 @@ If you have lower accuracy requirements you can even try doing a small oversampl
 
 We retrieved some early results on the relationship between limit and oversampling using the the DBPedia OpenAI 1M vector dataset. We ran all these experiments on a Qdrant instance where 100K vectors were indexed and used 100 random queries. 
 
-We varied the 3 parameters that will affect query time and accuracy: limit, rescore and oversampling. We offer these as an initial exploration of this new feature. You are highly encouraged to reproduce these experiments with your data sets.
+We varied the 3 parameters that will affect query time and accuracy: limit, rescore and oversampling. We offer these as an initial exploration of this new feature. You are highly encouraged to reproduce these experiments with your datasets.
 
 > Aside: Since this is a new innovation in vector databases, we are keen to hear feedback and results. [Join our Discord server](https://discord.gg/Qy6HCJK9Dc) for further discussion! 
 
@@ -231,6 +231,6 @@ If you determine that binary quantization is appropriate for your datasets and q
 
 Binary quantization is exceptional if you need to work with large volumes of data under high recall expectations. You can try this feature either by spinning up a [Qdrant container image](https://hub.docker.com/r/qdrant/qdrant) locally or, having us create one for you through a [free account](https://cloud.qdrant.io/signup) in our cloud hosted service. 
 
-The article gives examples of data sets and configuration you can use to get going. Our documentation covers [adding large datasets to Qdrant](/documentation/tutorials-develop/bulk-upload/) to your Qdrant instance as well as [more quantization methods](/documentation/manage-data/quantization/).
+The article gives examples of datasets and configuration you can use to get going. Our documentation covers [adding large datasets to Qdrant](/documentation/tutorials-develop/bulk-upload/) to your Qdrant instance as well as [more quantization methods](/documentation/manage-data/quantization/).
 
 If you have any feedback, drop us a note on Twitter or LinkedIn to tell us about your results. [Join our lively Discord Server](https://discord.gg/Qy6HCJK9Dc) if you want to discuss BQ with like-minded people!

qdrant-landing/content/articles/product-quantization.md

@@ -224,7 +224,7 @@ In circumstances that do not align with the above, Scalar Quantization should be
 ## Using Qdrant for Product Quantization
 
 
-If you’re already a Qdrant user, we have, documentation on [Product Quantization](/documentation/manage-data/quantization/#setting-up-product-quantization) that will help you to set and configure the new quantization for your data and achieve even 
+If you’re already a Qdrant user, our documentation on [Product Quantization](/documentation/manage-data/quantization/#setting-up-product-quantization) will help you set and configure the new quantization for your data and achieve even 
 up to 64x memory reduction.
 
 Ready to experience the power of Product Quantization? [Sign up now](https://cloud.qdrant.io/signup) for a free Qdrant demo and optimize your data management today!

qdrant-landing/content/articles/turboquant-quantization.md

@@ -46,7 +46,7 @@ To enable TurboQuant, specify it in the `quantization_config` section of the col
 
 When enabling TurboQuant on an existing collection, use a `PATCH` request, or the corresponding `update_collection` method in any client SDK.
 
-The `bits` field controls encoding bit depth. It defaults to `bits4`. Available values: `bits4`, `bits2`, `bits1_5`, and `bits1`. Lower bit depths offer higher compression at the cost of accuracy. See the [benchmarks](#detailed-benchmarks) for the recall trade-off on each bit width. The full reference is in [the quantization docs](https://qdrant.tech/documentation/guides/quantization/).
+The `bits` field controls encoding bit depth. It defaults to `bits4`. Available values: `bits4`, `bits2`, `bits1_5`, and `bits1`. Lower bit depths offer higher compression at the cost of accuracy. See the [benchmarks](#detailed-benchmarks) for the recall trade-off on each bit width. The full reference is in [the quantization docs](https://qdrant.tech/documentation/manage-data/quantization/).
 
 ## At a Glance
 
@@ -124,7 +124,7 @@ Truly isotropic data matches the theoretical Gaussian quantiles, the formula col
 
 ### L2 and Unnormalized Dot
 
-Vanilla TurboQuant assumes all inputs live on the unit sphere; that is, cosine distance only. We extend the scoring mechanism and unlock L2 and unnormalized dot by *storing the original L2 norm*, normalizing the vectors and then apply the L2 norm back during scoring.
+Vanilla TurboQuant assumes all inputs live on the unit sphere; that is, cosine distance only. We extend the scoring mechanism and unlock L2 and unnormalized dot by *storing the original L2 norm*, normalizing the vectors, and then applying the L2 norm back during scoring.
 
 L2 distances are reconstructed via the identity `‖q − v‖² = ‖q‖² + ‖v‖² − 2⟨q, v⟩ = ‖q‖² + ‖v‖² − 2 ‖v‖ ‖q‖ ⟨q_normalized, v_normalized⟩`, where all components on the right-hand side are already available.