[ready to merge] Blog edited: prompt engineering techniques

bifrost/app/blog/blogs/chain-of-thought-prompting/src.mdx

@@ -292,11 +292,11 @@ Standard prompting methods mainly focus on getting the right answer from the LLM
 
 ### CoT vs. Few-Shot Prompting
 
-Both techniques use examples, but they differ in an important way. Few-shot prompting gives the model just a few examples that are similar to what you want it to do. For instance, it might show a few solutions to problems that are alike.
+Both techniques use examples, but they differ in an important way. <a href="https://docs.helicone.ai/guides/prompt-engineering/implement-few-shot-learning" target="_blank">Few-shot prompting</a> gives the model just a few examples that are similar to what you want it to do. For instance, it might show a few solutions to problems that are alike.
 
 ### CoT vs. Tree-of-Thought Prompting
 
-Both Chain-of-Thought (CoT) and Tree-of-Thought (ToT) are prompting strategies that can be applied to large language models to improve their reasoning capabilities. CoT follows a linear approach where each step builds directly on previous reasoning. ToT uses a tree-like structure where multiple reasoning paths can be explored simultaneously.
+Both Chain-of-Thought (CoT) and <a href="https://www.helicone.ai/blog/tree-of-thought-prompting" target="_blank">Tree-of-Thought (ToT)</a> are prompting strategies that can be applied to large language models to improve their reasoning capabilities. CoT follows a linear approach where each step builds directly on previous reasoning. ToT uses a tree-like structure where multiple reasoning paths can be explored simultaneously.
 
 While both enhance problem-solving, they differ in complexity. CoT uses straightforward sequential reasoning, while ToT employs search algorithms like breadth-first search (BFS) and depth-first search (DFS) to systematically explore multiple solution paths. This makes ToT more computationally intensive but potentially more thorough for complex problems.
 
@@ -340,8 +340,8 @@ Chain-of-Thought prompting fundamentally changes how we interact with LLMs. By b
 
 ### You might find these useful:
 
-- **<a href="https://docs.helicone.ai/guides/prompt-engineering/overview" target="_blank">Other Prompt Engineering Techniques</a>**
-- **<a href="https://www.helicone.ai/blog/prompt-engineering-tools" target="_blank">Prompt Engineering Overview: All You Need to Know</a>**
+- **<a href="https://www.helicone.ai/blog/prompt-engineering-tools" target="_blank">Prompt Engineering Tools & Techniques</a>**
+- **<a href="https://www.helicone.ai/blog/test-your-llm-prompts" target="_blank">How to Test Your LLM Prompts</a>**
 - **<a href="https://www.helicone.ai/blog/prompt-management" target="_blank">What is Prompt Management?</a>**
 
 <Questions />

bifrost/app/blog/blogs/product-hunt-experience/metadata.json

@@ -6,6 +6,6 @@
   "images": "/static/blog/product-hunt-experience/PH-cover.webp",
   "time": "6 minute read",
   "author": "Lina Lam",
-  "date": "Aug 26, 2024", 
+  "date": "August 26, 2024", 
   "badge": "insight"
 }

bifrost/app/blog/blogs/prompt-engineering-tools/metadata.json

@@ -1,10 +1,10 @@
 {
-  "title": "Prompt Engineering: Tools, Best Practices, and Techniques",
-  "title1": "Prompt Engineering: Tools, Best Practices, and Techniques",
-  "title2": "Prompt Engineering: Tools, Best Practices, and Techniques",
+  "title": "Prompt Engineering Tools & Techniques [Updated Jan 2025]",
+  "title1": "Prompt Engineering Tools & Techniques [Updated Jan 2025]",
+  "title2": "Prompt Engineering Tools & Techniques [Updated Jan 2025]",
   "description": "Writing effective prompts is a crucial skill for developers working with large language models (LLMs). Here are the essentials of prompt engineering and the best tools to optimize your prompts.",
-  "images": "/static/blog/prompt-engineering-tools/cover.webp",
-  "time": "4 minute read",
+  "images": "/static/blog/prompt-engineering-tools/prompt-engineering-cover.webp",
+  "time": "8 minute read",
   "author": "Lina Lam",
   "date": "September 12, 2024", 
   "badge": "guide"

bifrost/app/blog/blogs/prompt-management/metadata.json

@@ -6,6 +6,6 @@
   "images": "/static/blog/prompt-management/cover.webp",
   "time": "5 minute read",
   "author": "Lina Lam",
-  "date": "Aug 1, 2024", 
+  "date": "August 1, 2024", 
   "badge": "guide"
 }

bifrost/app/blog/blogs/prompt-management/src.mdx

@@ -15,7 +15,7 @@ In this blog, we will dive into the challenges of managing prompts and what to l
 
 Large Language Models (LLMs) can be taught to perform new tasks using **<span style={{color: '#0ea5e9'}}>in-context learning</span>**, which involves feeding a prompt with instructions and/or examples. This method allows LLMs to perform tasks without needing additional training or parameter updates.
 
-**Prompt engineering is crucial for optimizing the model outputs.** Anyone working with AI products can be involved in crafting prompts using various techniques -- both developers and non-technical users.
+**<a href="https://www.helicone.ai/blog/prompt-engineering-tools" target="_blank" rel="noopener">Prompt engineering</a> is crucial for optimizing the model outputs.** Anyone working with AI products can be involved in crafting prompts using various techniques -- both developers and non-technical users.
 
 ## What is Prompt Management?
 
@@ -80,14 +80,14 @@ That's why Helicone was designed to provide full prompt ownership and the easies
     target="_blank"
     rel="noopener"
   >
-    Manage Prompts in Helicone
+    Docs: Create Prompts in Helicone
   </a>
 - <a
     href="https://docs.helicone.ai/features/experiments"
     target="_blank"
     rel="noopener"
   >
-    Creating Prompt Experiments in Helicone
+    Docs: Run Prompt Experiments in Helicone
   </a>
 - <a
     href="https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/"

bifrost/app/blog/blogs/slash-llm-cost/metadata.json

@@ -6,6 +6,6 @@
   "images": "/static/blog/slash-llm-cost/slash-cover.webp",
   "time": "6 minute read",
   "author": "Lina Lam",
-  "date": "Aug 30, 2024", 
+  "date": "August 30, 2024", 
   "badge": "how-to"
 }

bifrost/app/blog/blogs/tree-of-thought-prompting/src.mdx

@@ -1,10 +1,10 @@
-While the <a href="https://www.helicone.ai/blog/chain-of-thought-prompting" target="_blank">Chain of Thought (CoT)</a> prompting technique allows LLMs to thrive at step-wise reasoning problems, its inability to strategically look ahead and weigh different alternatives makes it fall short on tasks requiring decision-making.
+While the Chain of Thought (CoT) prompting technique allows LLMs to thrive at step-wise reasoning problems, its inability to strategically look ahead and weigh different alternatives makes it fall short on tasks requiring decision-making.
 
 The Tree of Thought (ToT) prompting technique unlocks new neural pathways for LLMs, encouraging them to explore multiple thoughts and self-evaluate at each step, even as they look ahead or backtrack to determine the next best move.
 
 ![Tree-of-Thought Prompting - Helicone](/static/blog/tree-of-thought/tree-of-thought-cover.webp)
 
-According to <a href="https://arxiv.org/abs/2305.10601" target="_blank" rel="noopener">Yao et el (2023)</a>, the Tree of Thought (ToT) is a prompting framework that generalizes over the Chain-of-Thought (CoT) technique breaking the token-level, left-to-right decision-making barrier. This technique combines advanced search algorithms with the innate self-evaluative properties of LLMs to implement deliberate decision-making.
+According to <a href="https://arxiv.org/abs/2305.10601" target="_blank" rel="noopener">Yao et el (2023)</a>, the Tree of Thought (ToT) is a prompting framework that generalizes over the <a href="https://www.helicone.ai/blog/chain-of-thought-prompting" target="_blank">Chain-of-Thought (CoT)</a> technique breaking the token-level, left-to-right decision-making barrier. This technique combines advanced search algorithms with the innate self-evaluative properties of LLMs to implement deliberate decision-making.
 
 In this article, we will explain Tree of Thought prompting, examine how it works, and compare its performance to other prompting techniques.
 
@@ -20,7 +20,7 @@ In this article, we will explain Tree of Thought prompting, examine how it works
 
 ## What is Tree of Thought Prompting?
 
-Tree of Thought (ToT) prompting is a new framework for language model inference proposed by <a href="https://arxiv.org/abs/2305.10601" target="_blank" rel="noopener">Yao et el (2023)</a> and <a href="https://arxiv.org/abs/2305.08291" target="_blank" rel="noopener">Long (2023)</a>, amongst other researchers in May 2023. The novel prompting technique uses intermediate reasoning steps to give LLMs complex, strategic reasoning power.
+Tree of Thought (ToT) prompting is a new framework for language model inference proposed by Yao et el (2023) and <a href="https://arxiv.org/abs/2305.08291" target="_blank" rel="noopener">Long (2023)</a>, amongst other researchers in May 2023. The novel prompting technique uses intermediate reasoning steps to give LLMs complex, strategic reasoning power.
 
 This technique takes on a human-like approach to problem-solving (trial and error), exhaustively working through every possible outcome in a problem/solution space. The computation progresses in a tree-like manner, following the most likely step at each turn, and backtracking when necessary until it finds the correct solution.
 
@@ -66,7 +66,7 @@ _Source: <a href="http://medium.com/@kapildevkhatik2" target="_blank" rel="noope
 
 ![Tree-of-Thought Prompting - How it compares with other prompting techniques](/static/blog/tree-of-thought/how-tot-compare.webp)
 
-_Source: <a href="https://arxiv.org/abs/2305.10601" target="_blank" rel="noopener">Yao et el. (2023)</a>_
+_Source: Yao et el. (2023)_
 
 At a high level, ToT helps LLMs achieve deliberate reasoning by:
 
@@ -84,9 +84,9 @@ Here's a schematic demonstration of how the framework combines the DFS search st
 
 ![Tree-of-Thought Framework](/static/blog/tree-of-thought/tot-framework.webp)
 
-_Source: <a href="https://arxiv.org/abs/2305.10601" target="_blank" rel="noopener">Yao et el. (2023)</a>_
+_Source: Yao et el. (2023)_
 
-**<a href="https://arxiv.org/abs/2305.08291" target="_blank" rel="noopener">Long (2023)</a>** augments LLMs with several modules including a ToT controller, which enables it to solve more specific problems. More precisely, this contraption combines reinforced learning with ToT, encouraging the controller to self-learn as it consumes data sets over time.
+Long (2023) augments LLMs with several modules including a ToT controller, which enables it to solve more specific problems. More precisely, this contraption combines reinforced learning with ToT, encouraging the controller to self-learn as it consumes data sets over time.
 
 In addition to the ToT controller, this suped-up LLM uses a memory module to track preceding token sequences. That way, it can easily retrace its steps and explore new directions. ToT-enabled LLMs can solve more complex puzzle games like Sudoku.
 
@@ -128,7 +128,7 @@ Instructing ChatGPT to provide diverse chains of thought that reach a consensus
 
 Since the precision of an LLM output depends strongly on how well the prompt is constructed, using well-refined ToT prompts improves its performance.
 
-**<a href="https://docs.helicone.ai/features/experiments" target="_blank" rel="noopener">Helicone's Experiments</a>** help users simplify the prompt creation and optimization process. This tool provides a playground where you can safely experiment with prompts and measure their success before pushing your code to a live environment.
+Helicone's **<a href="https://docs.helicone.ai/features/experiments" target="_blank" rel="noopener">Experiments</a>** help users simplify the prompt creation and optimization process. This tool provides a playground where you can safely experiment with prompts and measure their success before pushing your code to a live environment.
 
 ![Tree-of-Thought Prompting - Running Experiments in Helicone](/static/blog/tree-of-thought/tot-experiments.webp)
 
@@ -178,9 +178,9 @@ You can always add more input rows or evaluators to your experiment for more com
 
 Above is an example of forking an existing prompt to create a new variation.
 
-## Tree of Thought Prompting Vs. Other Prompting Techniques (Benchmarks)
+## Tree of Thought Prompting vs. Other Prompting Techniques
 
-Compared to <a href="https://docs.helicone.ai/guides/prompt-engineering/overview" target="_blank" rel="noopener">preceding prompting techniques</a>, ToT shows massive improvement at planning/searching problems such as the game of 24, crossword puzzles, and creative writing, according to Yao et el. (2023).
+Compared to <a href="https://helicone.ai/blog/prompt-engineering-tools" target="_blank" rel="noopener">other prompting techniques</a>, ToT shows massive improvement at planning/searching problems such as the game of 24, crossword puzzles, and creative writing, according to Yao et el. (2023).
 
 ![Tree-of-Thought Prompting - Benchmarks](/static/blog/tree-of-thought/tot-benchmarks.webp)
 
@@ -239,3 +239,5 @@ Based on Yao et el. (2023) benchmark tests, the Tree of Thought framework/prompt
   >
     Prompt Evaluation Explained: Random Sampling vs. Golden Datasets
   </a>
+
+<Questions />

bifrost/app/blog/page.tsx

@@ -254,6 +254,11 @@ const blogContent: BlogStructure[] = [
       folderName: "chain-of-thought-prompting",
     },
   },
+  {
+    dynmaicEntry: {
+      folderName: "llm-api-providers",
+    },
+  },
   {
     dynmaicEntry: {
       folderName: "rag-chunking-strategies",
@@ -271,12 +276,12 @@ const blogContent: BlogStructure[] = [
   },
   {
     dynmaicEntry: {
-      folderName: "claude-3.5-sonnet-vs-openai-o1",
+      folderName: "crewai-vs-dify-ai",
     },
   },
   {
     dynmaicEntry: {
-      folderName: "crewai-vs-dify-ai",
+      folderName: "claude-3.5-sonnet-vs-openai-o1",
     },
   },
   {
@@ -306,12 +311,12 @@ const blogContent: BlogStructure[] = [
   },
   {
     dynmaicEntry: {
-      folderName: "crewai-vs-autogen",
+      folderName: "prompt-evaluation-for-llms",
     },
   },
   {
     dynmaicEntry: {
-      folderName: "prompt-evaluation-for-llms",
+      folderName: "crewai-vs-autogen",
     },
   },
   {
@@ -324,11 +329,6 @@ const blogContent: BlogStructure[] = [
       folderName: "llamaindex-vs-langchain",
     },
   },
-  {
-    dynmaicEntry: {
-      folderName: "llm-api-providers",
-    },
-  },
   {
     dynmaicEntry: {
       folderName: "when-to-finetune",