Prompt Engineering

Prompt classification

Plan Like a Graph

Plan Like a Graph (PLaG) that converts naturalistic questions to equivalent graph problems, which significantly improves the performance of LLMs in asynchronous planning tasks.

Let's say we have an asynchronous planning task where we need to bake a cake, frost it, and then decorate it, and we have the following time durations and constraints:

- Mixing the cake batter takes 10 minutes
- Baking the cake takes 30 minutes
- Frosting the cake takes 5 minutes
- Decorating the cake takes 15 minutes
- Baking the cake must be done after mixing the batter
- Frosting the cake must be done after baking the cake
- Decorating the cake must be done after frosting it

To use the PLaG technique, we would first convert this task into a graph representation, where the nodes represent the steps in the task and the edges represent the constraints between them. The resulting graph would look like this:


`1Mix batter (10 min) ----> Bake cake (30 min) ----> Frost cake (5 min) ----> Decorate cake (15 min)`

Next, we would prompt the LLM with the task description and the graph representation, instructing it to reason based on the graph. For example, the prompt might look like this:

"Consider the following task: Bake a cake, frost it, and then decorate it. The steps and time durations are as follows:

- Mixing the cake batter takes 10 minutes
- Baking the cake takes 30 minutes
- Frosting the cake takes 5 minutes
- Decorating the cake takes 15 minutes

The constraints between the steps are as follows:

- Baking the cake must be done after mixing the batter
- Frosting the cake must be done after baking the cake
- Decorating the cake must be done after frosting it

Use the following graph to reason about the task and determine the shortest possible time needed to complete it:

`1Mix batter (10 min) ----> Bake cake (30 min) ----> Frost cake (5 min) ----> Decorate cake (15 min)`

By providing the LLM with a graph representation of the task, we can help it reason more effectively about the constraints and time durations involved, leading to more accurate predictions about the shortest possible time needed to complete the task.

Prompt Pattern

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Input Semantics: Changing how the LLM understands input.
Output Customization: Controlling the format, structure, or other aspects of the LLM’s output.
Error Identification: Helping users identify and correct errors in the LLM’s output.
Prompt Improvement: Enhancing the quality of both the user’s prompts and the LLM’s responses.
Interaction: Changing how the user interacts with the LLM.

Pattern Category	Prompt Pattern
Input Semantics	Meta Language Creation
Output Customization	Output Automater Persona Visualization Generator Recipe Template
Error Identification	Fact Check List Reflection
Prompt Improvement	Question Refinement Alternative Approaches Cognitive Verifier Refusal Breaker
Interaction	Flipped Interaction Game Play Infinite Generation
Context Control	Context Manager

Dspy

Dspy(Declarative Self-improving Language Programs) make it easy to follow the data science process when building LM apps

Workflow

define your task
collect some data and LM/RM connection
Define your metrics
setup a pipeline
compile/optimize the program
Save your experiment and iterate

Components of Dspy

Signatures: Define the input-output structure for model interactions, ensuring clarity and consistency across different modules. (question → answer , doc→ summary)
Modules: Encapsulate specific tasks or operations as reusable components. This modular design enhances the flexibility and scalability of applications built with DSPy.
Teleprompters: Manage the execution flow of modules, allowing for sophisticated sequencing and optimization of interactions with language models.
Hand-written prompts and fine-tuning are abstracted and replaced by signatures
Prompting techniques, such as Chain of Thought or ReAct, are abstracted and replaced by modules
Manual prompt engineering is automated with optimizers teleprompters and a DSPy Compiler

Singature

signature is a short function that specifies what a transformation does rather than how to prompt the LM to do it (e.g., “consume questions and context and return answers”).

"context, question"    -> "answer"
Input seprated by comma | output
 
"question -> answer" 
 
"long-document -> summary" 
 
"context, question -> answer"

class GenerateAnswer(dspy.Signature): 
	"""Answer questions with short factoid answers.""" 
	context = dspy.InputField(desc="may contain relevant facts") 
	question = dspy.InputField() 
	answer = dspy.OutputField(desc="often between 1 and 5 words")
 
predict = dspy.predict(GenerateAnswer)
prediction = predict(question="how many hydrogent present in water",context="")
 
print(predict.answer)
 
turbo.inspect_history(n=10) #"Prints the last n prompts and their completions

below data will send to LLM and context,question and answer to get by using pydantic

Answer questions with short factoid answers. 
 
---
 
Follow the following format.
 
Context: may contain relevant facts
Question: ${question}
Answer: often between 1 and 5 words
 
---
 
Context: 
Question: how many hydrogent present in water
Answer:Context: 
Question: how many hydrogent present in water
Answer: Two  #answer from chat gpt

Modules: Abstracting prompting techniques

Modules in DSPy are templated and parameterized to abstract these prompting techniques. This means that they are used to adapt DSPy signatures to a task by applying prompting, fine-tuning, augmentation, and reasoning techniques.

# Option 1: Pass minimal signature to ChainOfThought module 
generate_answer = dspy.ChainOfThought("context, question -> answer") 
 
# Option 2: Or pass full notation signature to ChainOfThought module 
generate_answer = dspy.ChainOfThought(GenerateAnswer) 
 
# Call the module on a particular input. 
pred = generate_answer(context = "Which meant learning Lisp, since in those days Lisp was regarded as the language of AI.", question = "What programming language did the author learn in college?")
 
print(pred.answer)

Below prompt will send to LLM for above code

Given the fields `context`, `question`, produce the fields `answer`.
 
---
 
Follow the following format.
 
Context: ${context}
 
Question: ${question}
 
Reasoning: Let's think step by step in order to ${produce the answer}. We ...
 
Answer: ${answer}
 
---
 
Context: Which meant learning Lisp, since in those days Lisp was regarded as the language of AI.
 
Question: What programming language did the author learn in college?
 
Reasoning: Let's think step by step in order to
 
Context: Which meant learning Lisp, since in those days Lisp was regarded as the language of AI.
 
Question: What programming language did the author learn in college?
 
Reasoning: Let's think step by step in order to find the answer to the question. The context states that the author learned Lisp in college. 
 
Answer: Lisp

dspy.Predict: Processes the input and output fields, generates instructions, and creates a template for the specified signature.
dspy.ChainOfThought: Inherits from the Predict module and adds functionality for “Chain of Thought” processing.
dspy.ChainOfThoughtWithHint: Inherits from the Predict module and enhances the ChainOfThought module with the option to provide hints for reasoning.
dspy.MultiChainComparison: Inherits from the Predict module and adds functionality for multiple chain comparisons.
dspy.Retrieve: Retrieves passages from a retriever module.
dspy.ReAct: Designed to compose the interleaved steps of Thought, Action, and Observation.

You can chain these modules together in classes that are inherited from dspy.Module and take two methods. You might already notice a syntactic similarity to PyTorch

__init__(): Declares the used submodules.
forward(): Describes the control flow among the defined sub-modules.

class RAG(dspy.Module): 
 def __init__(self, num_passages=3): 
	super().__init__() 
	self.retrieve = dspy.Retrieve(k=num_passages) 
	self.generate_answer = dspy.ChainOfThought(GenerateAnswer)
 
def forward(self, question): 
	  context = self.retrieve(question).passages 
	  prediction = self.generate_answer(context=context, question=question) 
	  return dspy.Prediction(context=context, answer=prediction.answer)

Optimizer

A DSPy optimizer is an algorithm that can tune the parameters of a DSPy program (i.e., the prompts and/or the LM weights) to maximize the metrics you specify, like accuracy.

DSPy programs consist of multiple calls to LMs, stacked together as [DSPy modules]. Each DSPy module has internal parameters of three kinds: (1) the LM weights, (2) the instructions, and (3) demonstrations of the input/output behavior.

Given a metric, DSPy can optimize all of these three with multi-stage optimization algorithms. These can combine gradient descent (for LM weights) and discrete LM-driven optimization, i.e. for crafting/updating instructions and for creating/validating demonstrations. DSPy Demonstrations are like few-shot examples, but they’re far more powerful. They can be created from scratch, given your program, and their creation and selection can be optimized in many effective ways.

Automatic Few-Shot Learning

LabeledFewShot
BootstrapFewShot
BootstrapFewShotWithRandomSearch
BootstrapFewShotWithOptuna
KNNFewShot

Internal of DSPY

Dspy uses pydantic Pydantic for dspy.InputField and other things

Reflection Fine-Tuning

Reflection is the new fine-tuning technique where the fine-tuning prompt is changed a bit to incorporate self reflection while training the LLM, improving the results by a big margin. Prompt

You are a world-class AI system, capable of complex reasoning and reflection.  
Reason through the query inside <thinking> tags, and  
then provide your final response inside <output> tags.  
If you detect that you made a mistake in your reasoning at any point,  
correct yourself inside <reflection> tags.

The model begins by generating its reasoning within <thinking> tags. This section contains the model’s internal thought process as it analyzes the input query.
Within the <thinking> section, the model may include <reflection> tags if it identifies any mistakes in its reasoning. This indicates that the model is capable of recognizing errors and will attempt to correct them before finalizing its answer.

Chain-of-thought (CoT)

By leveraging in-context learning abilities, CoT prompting encourages a language model to more effectively solve complex problems by outputting along with its solution a corresponding “chain of thought” (i.e., a step-by-step explanation for how the problem was solved). The model can be prompted to generate a chain of thought via a few-shot learning approach that provides several chain of thought exemplars; see above. The CoT technique is most effective when the map from input to output is highly non-trivial; e.g., math or multi-step reasoning problems.

Note: use COT for mathematical and reasoning where the perform good. check out here for more

Prompt

 You are an AI assistant that uses a Chain of Thought (CoT) approach with reflection to answer queries. Follow these steps:

        1. Think through the problem step by step within the <thinking> tags.
        2. Reflect on your thinking to check for any errors or improvements within the <reflection> tags.
        3. Make any necessary adjustments based on your reflection.
        4. Provide your final, concise answer within the <output> tags.

        Important: The <thinking> and <reflection> sections are for your internal reasoning process only. 
        Do not include any part of the final answer in these sections. 
        The actual response to the query must be entirely contained within the <output> tags.

        Use the following format for your response:
        <thinking>
        [Your step-by-step reasoning goes here. This is your internal thought process, not the final answer.]
        <reflection>
        [Your reflection on your reasoning, checking for errors or improvements]
        </reflection>
        [Any adjustments to your thinking based on your reflection]
        </thinking>
        <output>
        [Your final, concise answer to the query. This is the only part that will be shown to the user.]
        </output>

Auto COT

Auto-CoT paradigm to automatically construct demonstrations with questions and reasoning chains. Specifically, Auto-CoT leverages LLMs with the “Let’s think step by step” prompt to generate reasoning chains for demonstrations one by one, and sending that COT with question to LLM as few shot prompting

Self-consistency with (CoT)

Need to add notes

ReAct

ReAct is a general paradigm that combines reasoning and acting with LLMs. ReAct prompts LLMs to generate verbal reasoning traces and actions for a task.

it to be better than chain-of-thought prompting

- Thought 1: I need to search for “Colorado orogeny,” identify the area covered by the eastern sector, and then determine the elevation range of that area.
- 
- Action 1: Search [Colorado orogeny]
- Observation 1: The Colorado orogeny refers to a phase of mountain formation in Colorado and nearby regions.
- 
- Thought 2: The eastern sector isn’t mentioned here. I should look up “eastern sector.”
- Action 2: Lookup [eastern sector]
- Observation 2: The eastern sector extends into the High Plains and is known as the Central Plains orogeny.

- Thought 3: The eastern sector of the Colorado orogeny reaches into the High Plains. I should search for the High Plains and find its elevation range.
- Action 3: Search [High Plains]
- Observation 3: High Plains refers to one of two distinct land regions.

- Thought 4: I should search specifically for “High Plains (United States).”

- Action 4: Search [High Plains (United States)]
- Observation 4: The High Plains are a part of the Great Plains, with elevations rising from around 1,800 to 7,000 ft (550 to 2,130 m).

- Thought 5: The elevation range of the High Plains is 1,800 to 7,000 ft, so that’s the answer.
- Action 5: Finish [1,800 to 7,000 ft]

Tree of Thoughts

Tree of Thoughts is an innovative approach to AI reasoning that expands upon the Chain of Thought (CoT) methodology. While CoT prompts an AI to explain its thinking in a linear fashion, ToT takes this a step further by encouraging the AI to explore multiple paths of reasoning simultaneously, much like a tree branching out in various directions.

check out implementation here

Prompt


**You are an expert problem-solving agent** designed to not only solve complex problems but also critically evaluate the quality of your thought process and final answers. 

Your task is to follow a structured approach to generate solutions, assess your thoughts, and provide a rating for each on a scale of 0.1 to 1.0. This rating should reflect the accuracy and quality of your reasoning and final answer.

### Instructions:

1. **Understand the Problem:**  
   - Carefully analyze the problem provided by the user.  
   - Break down the problem into smaller, manageable parts if necessary.  
   - Formulate a clear understanding of the problem before proceeding.

2. **Generate Thoughts:**  
   - Create multiple thoughts or steps toward solving the problem.  
   - For each thought, document your reasoning, ensuring that it is logical and well-founded.

3. **Self-Evaluation:**  
   - After generating each thought, evaluate its accuracy and quality.  
   - Assign an evaluation score between 0.1 and 1.0. Use the following guidelines:  
     - **0.1 to 0.4:** The thought is flawed, inaccurate, or incomplete.  
     - **0.5 to 0.7:** The thought is partially correct but may lack detail or full accuracy.  
     - **0.8 to 1.0:** The thought is accurate, complete, and well-reasoned.

4. **Generate Final Answer:**  
   - Based on your thoughts, synthesize a final answer to the problem.  
   - Ensure the final answer is comprehensive and addresses all aspects of the problem.

5. **Final Evaluation:**  
   - Evaluate the overall quality and accuracy of your final answer.  
   - Provide a final evaluation score based on the same 0.1 to 1.0 scale.

Re-Reading Improves Reasoning in Large Language Models

The core concept of the paper “Re-Reading Improves Reasoning in Large Language Models” is that repeating the input question can enhance the reasoning capabilities of Large Language Models (LLMs),

Unlike many thought-eliciting prompting methods (e.g., Chain-of-Thought) that focus on structuring the output, RE2 focuses on improving how the LLM processes the input This is analogous to how understanding the question is paramount to solving a problem for humans.

Re-Reading + COT

Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? 

Read the question again: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? A:

Let’s think step by step.

potentially improve the reasoning performance of Large Language Models (LLMs).

Summarization

https://towardsdatascience.com/summarize-podcast-transcripts-and-long-texts-better-with-nlp-and-ai-e04c89d3b2cb

Claude Meta Prompt

Claude have written a prompt that will help to get perfect prompt in XML check here

Claude Prompt Tips

Some takeaways you can use for writing your long-context Q&A prompts:

Use many examples and the scratchpad for best performance on both context lengths.
Pulling relevant quotes into the scratchpad is helpful in all head-to-head comparisons. It comes at a small cost to latency, but improves accuracy. In Claude Instant’s case, the latency is already so low that this shouldn’t be a concern.
Contextual examples help on both 70K and 95K, and more examples is better.
Generic examples on general/external knowledge do not seem to help performance.

I need to write a blog post on the topic of [integrating enterprise data with an LLM] for my AI solutions company, AI Disruptor.

Begin in <scratchpad> tags and write out and brainstorm in a couple paragraphs your plan for how you will create an informative and engaging blog. Also brainstorm how you will create a CTA at the end for our company, AI Disruptor.

To get json response start the conversion for assistant with { like below


user : "prompt"
asssistant: "{" -> which tell the model to start with { need to return as json

if claude saying text after json we can use stop_sequences ask the model to wrap a json with json tag like <json></json> and we can give stop_sequences as </json>

Prompt compression

Prompt compression is a technique used in natural language processing (NLP) to optimize the inputs given to LLMs by reducing their length without significantly altering the quality and relevance of the output.

gpttrim (By tokenizing, stemming, and removing spaces)
LLMLingua (A LLM developed by microsoft open source which will help to reduce the prompt)

FrugalGPT

FrugalGPT is a framework proposed by Lingjiao Chen, Matei Zaharia and James Zou from Stanford University in their 2023 paper “FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance”. The paper outlines strategies for more cost-effective and performant usage of large language model (LLM) APIs.

The core of FrugalGPT revolves around three key techniques for reducing LLM inference costs:

Prompt Optimization

Prompt Adaptation: FrugalGPT wants us to either reduce the size of the prompt OR combine similar prompts together. Core idea is to minimise tokens and thus reduce LLM costs Example for email classification task we can only pick the top k similar examples. using similarity . FrugalGPT suggests identifying the best examples to be used instead of all of them.

Combine similar requests together : LLMs have been found to retain context for multiple tasks together and FrugalGPT proposes to use this to group multiple requests together thus decreasing the redundant prompt examples in each request.

Better utilize a smaller model with a more optimized prompt :

Example: from Claude 3.5 Sonnet to GPT-4o-mini — reducing costs massively while keeping quality high.

Compress the prompt : The compression process happens in three main steps:

Token Classification:
- The trained model processes each token in the original prompt and assigns a preserve or discard probability based on the token’s importance for preserving the meaning of the text.
Selection of Tokens:
- The target compression ratio is used to decide how many tokens to retain. For example, if a 2x compression ratio is desired, 50% of the original tokens will be retained. The model sorts the tokens based on their preserve probability and selects the top tokens to keep in the compressed prompt.
Preserve Token Order:
- After selecting the tokens to preserve, the original order of the tokens is maintained to ensure that the compressed prompt remains coherent and grammatically correct.

check out here

LLM Approximation

Cache LLM Requests : When the prompt is exactly the same, we can save the inference time and cost by serving the request from cache.

Fine-tune a smaller model in parallel : In a production environment, it can be massively beneficial to keep serving requests through a bigger model while continuously logging and fine-tuning a smaller model on those responses. We can then evaluate the results from the fine-tuned model and the larger model to determine when it make sense to switch.

LLM Cascade

The key idea is to sequentially query different LLMs based on the confidence of the previous LLM’s response. If a cheaper LLM can provide a satisfactory answer, there’s no need to query the more expensive models, thus saving costs.

In essence, the LLM cascade makes a request to the smallest model first, evaluates the response, and returns it if it’s good enough. Otherwise, it requests the next larger model and so on until a satisfactory response is obtained or the largest model is reached.

CO-STAR framework

(C) Context: Provide background information on the task

This helps the LLM understand the specific scenario being discussed, ensuring its response is relevant.

(O) Objective: Define what the task is that you want the LLM to perform

Being clear about your objective helps the LLM to focus its response on meeting that specific goal.

(S) Style: Specify the writing style you want the LLM to use

This could be a particular famous person’s style of writing, or a particular expert in a profession, like a business analyst expert or CEO. This guides the LLM to respond with the manner and choice of words aligned with your needs.

(T) Tone: Set the attitude of the response

This ensures the LLM’s response resonates with the intended sentiment or emotional context required. Examples are formal, humorous, empathetic, among others.

(A) Audience: Identify who the response is intended for

Tailoring the LLM’s response to an audience, such as experts in a field, beginners, children, and so on, ensures that it is appropriate and understandable in your required context.

(R) Response: Provide the response format

This ensures that the LLM outputs in the exact format that you require for downstream tasks. Examples include a list, a JSON, a professional report, and so on. For most LLM applications which work on the LLM responses programmatically for downstream manipulations, a JSON output format would be ideal.

> # CONTEXT # I want to advertise my company's new product. My company's name is Alpha and the product is called Beta, which is a new ultra-fast hairdryer.

> # OBJECTIVE # Create a Facebook post for me, which aims to get people to click on the product link to purchase it.

> # STYLE # Follow the writing style of successful companies that advertise similar products, such as Dyson.

> # TONE # Persuasive

> # AUDIENCE # My company's audience profile on Facebook is typically the older generation. Tailor your post to target what this audience typically looks out for in hair products.

> # RESPONSE # The Facebook post, kept concise yet impactful.

Instance-Adaptive prompting strategy

A prompt can vary depending on the specific instance we cannot use a same prompt for all use case

To overcome this limitation, Instance-Adaptive Prompting (IAP), was suggest which aims to select the most suitable prompt for each individual question, rather than relying on a single prompt for an entire task

How IAP Works:

The IAP strategy uses these insights to select an appropriate prompt for each question from a pool of candidate prompts. They propose two methods

Sequential Substitution (IAP-ss): The system tries prompts one by one, stopping when a prompt leads to good reasoning or all prompts are exhausted. for that they use Saliency Score
Majority Vote (IAP-mv): The system evaluates all candidate prompts and selects the one that consistently produces the best reasoning

Prompt Decomposition

Prompt Decomposition is the process of taking a complicated prompt and breaking it into multiple smaller parts. This is the same idea that is found in design theory and sometimes called task decomposition. Simply put, when we have a large complicated task, we break it down into multiple steps and each step is individually much easier.

Meta Prompting

It involves constructing a high-level “meta” prompt that instructs an LLM

prompt

You are Meta-Expert, an extremely clever expert with the unique ability to collaborate with multiple experts (such as Expert

Problem Solver, Expert Mathematician, Expert Essayist, etc.) to tackle any task and solve any complex problems. Some

experts are adept at generating solutions, while others excel in verifying answers and providing valuable feedback.

Note that you also have special access to Expert Python, which has the unique ability to generate and execute Python code

given natural-language instructions. Expert Python is highly capable of crafting code to perform complex calculations when

given clear and precise directions. You might therefore want to use it especially for computational tasks.

As Meta-Expert, your role is to oversee the communication between the experts, effectively using their skills to answer a

given question while applying your own critical thinking and verification abilities.

To communicate with a expert, type its name (e.g., "Expert Linguist" or "Expert Puzzle Solver"), followed by a colon ":", and

then provide a detailed instruction enclosed within triple quotes. For example:

Expert Mathematician:

"""

You are a mathematics expert, specializing in the fields of geometry and algebra.

Compute the Euclidean distance between the points (-2, 5) and (3, 7).

"""

Ensure that your instructions are clear and unambiguous, and include all necessary information within the triple quotes. You

can also assign personas to the experts (e.g., "You are a physicist specialized in...").

Interact with only one expert at a time, and break complex problems into smaller, solvable tasks if needed. Each interaction

is treated as an isolated event, so include all relevant details in every call.

If you or an expert finds a mistake in another expert's solution, ask a new expert to review the details, compare both

solutions, and give feedback. You can request an expert to redo their calculations or work, using input from other experts.

Keep in mind that all experts, except yourself, have no memory! Therefore, always provide complete information in your

instructions when contacting them. Since experts can sometimes make errors, seek multiple opinions or independently

verify the solution if uncertain. Before providing a final answer, always consult an expert for confirmation. Ideally, obtain or

verify the final solution with two independent experts. However, aim to present your final answer within 15 rounds or fewer.

Refrain from repeating the very same questions to experts. Examine their responses carefully and seek clarification if

required, keeping in mind they don't recall past interactions.

Present the final answer as follows:

>> FINAL ANSWER:

"""

[final answer]

"""

For multiple-choice questions, select only one option. Each question has a unique answer, so analyze the provided

information carefully to determine the most accurate and appropriate response. Please present only one solution if you

come across multiple options.

Prompt for generate System Prompt

Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
    - Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.
- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.
- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.
- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.
- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.
- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)
    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.
    - JSON should never be wrapped in code blocks (```) unless explicitly requested.

The final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no "---")

[Concise instruction describing the task - this should be the first line in the prompt, no section header]

[Additional details as needed.]

[Optional sections with headings or bullet points for detailed steps.]

# Steps [optional]

[optional: a detailed breakdown of the steps necessary to accomplish the task]

# Output Format

[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc]

# Examples [optional]

[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]
[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]

# Notes [optional]

[optional: edge cases, details, and an area to call or repeat out specific important considerations]

ChatML

ChatML (Chat Markup Language) is a lightweight markup format used by OpenAI to structure conversations between users and models, especially in chatbot-like environments. It is designed to define roles and organize the flow of conversation between different participants, such as system instructions, user inputs, and model responses.

In a typical ChatML format, the message blocks are defined by tags such as:

<|system|>: Instructions or setup given to the model (usually hidden from the user).
<|user|>: Represents what the user says.
<|assistant|>: Represents the assistant’s responses.
<im_start|> : the start of an interactive mode where messages will alternate between participants. It’s generally used to transition into the back-and-forth of a conversation.
<im_end|>

Tools

Zenbase

Developer tools and cloud infrastructure for perfectionists using LLMs. Zenbase takes care of the hassle of prompt engineering and model selection.

EvalLM

Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria

Prompt Hacking

output2prompt

The core idea behind output2prompt is clever in its simplicity. By analyzing patterns in the AI’s responses, another AI can infer the instructions that produced those responses.

My Thoughts

When you write a prompt think how it process and response by yourself it will give you a idea how your prompt will work and where to improve
Provide important thing at start of the prompt
Think as it just next word predictor not more then that so think in the way when writing prompt
Visulize attention mechnaism on the prompt
Tell how to handle negative else it will hallucinate
If you using too much example the response will be more genric based on the example so keep that in mind
use stop sequence if you want avoid unwanted text

Here are 5 papers you want to read to understand better how OpenAI o1 might work. Focusing on Improving LLM reasoning capabilities for complex tasks via training/RLHF, not prompting. 👀

Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking (https://lnkd.in/eCPaa-wc) from Stanford

Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents (https://lnkd.in/eebwEkPi) from MultiOn/Stanford

Let’s Verify Step by Step (https://lnkd.in/egf6EpMd) from OpenAI

V-STaR: Training Verifiers for Self-Taught Reasoners (https://lnkd.in/ebRcEKBn) from Microsoft, Mila**

Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning (https://lnkd.in/eeeaqm6x) from Notre Dam, Tencent

Prompt Engineering

Table of Contents

Prompt classification

Plan Like a Graph

Prompt Pattern

Dspy

Singature

Modules: Abstracting prompting techniques

Optimizer

Internal of DSPY

Reflection Fine-Tuning

Chain-of-thought (CoT)

Auto COT

Self-consistency with (CoT)

ReAct

Tree of Thoughts

Re-Reading Improves Reasoning in Large Language Models

Summarization

Claude Meta Prompt

Claude Prompt Tips

Prompt compression

FrugalGPT

Prompt Optimization

LLM Approximation

LLM Cascade

CO-STAR framework

Instance-Adaptive prompting strategy

Prompt Decomposition

Meta Prompting

Prompt for generate System Prompt

ChatML

Tools

Zenbase

EvalLM

Prompt Hacking

output2prompt

My Thoughts

Resources

Tools

Graph View

Table of Contents