Update renamed links

Author: subramen

recipes/quickstart/README.md

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 If you are new to developing with Meta Llama models, this is where you should start. This folder contains introductory-level notebooks across different techniques relating to Meta Llama.
 
 * The [](./Running_Llama3_Anywhere/) notebooks demonstrate how to run Llama inference across Linux, Mac and Windows platforms using the appropriate tooling.
-* The [](./prompt_engineering/Prompt_Engineering_with_Llama_3.ipynb) notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters.
-* The [](./inference/) folder contains scripts to deploy Llama for inference on server and mobile. See also [](../3p_integrations/vllm/) and [](../3p_integrations/tgi/) for hosting Llama on open-source model servers.
+* The [](./Prompt_Engineering_with_Llama_3.ipynb) notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters.
+* The [](./inference/) folder contains scripts to deploy Llama for inference on server and mobile. See also [](../3p_integration/vllm/) and [](../3p_integration/tgi/) for hosting Llama on open-source model servers.
 * The [](./RAG/) folder contains a simple Retrieval-Augmented Generation application using Llama 3.
 * The [](./finetuning/) folder contains resources to help you finetune Llama 3 on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-recipes finetuning code found in [](../../src/llama_recipes/finetuning.py) which supports these features: