Context Engineering by now. This text will cowl the important thing concepts behind creating LLM purposes utilizing Context Engineering ideas, visually clarify these workflows, and share code snippets that apply these ideas virtually.
Don’t fear about copy-pasting the code from this text into your editor. On the finish of this text, I’ll share the GitHub hyperlink to the open-source code repository and a hyperlink to my 1-hour 20-minute YouTube course that explains the ideas introduced right here in larger element.
Except in any other case talked about, all photos used on this article are produced by the creator and are free to make use of.
Let’s start!
What’s Context Engineering?
There’s a vital hole between writing easy prompts and constructing production-ready purposes. Context Engineering is an umbrella time period that refers back to the delicate artwork and science of becoming data into the context window of an LLM as it really works on a job.
The precise scope of the place the definition of Context Engineering begins and ends is debatable, however in accordance with this tweet from Andrej Karpathy, we will establish the next key factors:
- It isn’t simply atomic immediate engineering, the place you ask one query to the LLM and get a response
- It’s a holistic strategy that breaks up a bigger downside into a number of subproblems
- These subproblems might be solved by a number of LLMs (or brokers) in isolation. Every agent is supplied with the suitable context to hold out its job
- Every agent might be of acceptable functionality and measurement relying on the complexity of the duty.
- Intermediate steps that every agent can take to finish the duty – the context is not simply data we enter – it additionally contains intermediate tokens that the LLMs see throughout technology (eg. reasoning steps, software outcomes, and many others)
- The brokers are related with management flows, and we orchestrate precisely how data flows via our system
- The data accessible to the brokers can come from a number of sources – exterior databases with Retrieval-Augmented Technology (RAG), software calls (like net search), reminiscence programs, or traditional few-shot examples.
- Brokers can take actions whereas producing responses. Every motion the agent can take ought to be well-defined so the LLM can work together with it via reasoning and appearing.
- Moreover, programs have to be evaluated with metrics and maintained with observability. Monitoring token utilization, latency, and value to output high quality is a key consideration.
Vital: How this text is structured
All through this text, I might be referring to the factors above whereas offering examples of how they’re utilized in constructing actual purposes. Every time I accomplish that, I’ll use a block quote like this:
It’s a holistic strategy that breaks up a bigger downside into a number of subproblems
Once you see a quote on this format, the instance that follows will apply the quoted idea programmatically.
However earlier than that, we should ask ourselves one query…
Why not move all the things into the LLM?
Analysis has proven that cramming each piece of knowledge into the context of an LLM is way from very best. Although many frontier fashions do declare to assist “long-context” home windows, they nonetheless undergo from points like context poisoning or context rot.
(Supply: Chroma)
An excessive amount of pointless data in an LLM’s context can pollute the mannequin’s understanding, result in hallucinations, and lead to poor efficiency.
Because of this merely having a big context window isn’t sufficient. We want systematic approaches to context engineering.
Why DSPY

For this tutorial, I’ve chosen the DSPy framework. I’ll clarify the reasoning for this alternative shortly, however let me guarantee you that the ideas introduced right here apply to virtually any prompting framework, together with writing prompts in pure English.
DSPy is a declarative framework for constructing modular AI software program. They’ve neatly separated the 2 key features of any LLM job —
(a) the enter and output contracts handed right into a module,
and (b) the logic that governs how data flows.
Let’s see an instance!
Think about we need to use an LLM to jot down a joke. Particularly, we wish it to generate a setup, a punchline, and the complete supply in a comic’s voice.
Oh, and we additionally need the output in JSON format in order that we will post-process particular person fields of the dictionary after technology. For instance, maybe we need to print the punchline on a T-shirt (assume somebody has already written a handy operate for that).
system_prompt = """
You're a comic who tells jokes, you might be all the time humorous.
Generate the setup, punchline, and full supply within the comic's voice.
Output within the following JSON format:
{
"setup": <str>,
"punchline": <str>,
"supply": <str>
}
Your response ought to be parsable withou errors in Python utilizing json.hundreds().
"""
consumer = openai.Shopper()
response = consumer.chat.completions.create(
mannequin="gpt-4o-mini",
temperature = 1,
messages=[
{"role": "system", "content": system_prompt,
{"role": "user", "content": "Write a joke about AI"}
]
)
joke = json.hundreds(response.decisions[0].message.content material) # Hope for one of the best
print_on_a_tshirt(joke["punchline"])
Discover how we post-process the LLM’s response to extract the dictionary? What if one thing “dangerous” occurred, just like the LLM failing to generate the response within the desired format? Our total code would fail and there might be no printing on any T-shirts!
The above code can be fairly troublesome to increase. For instance, if we needed the LLM to do chain of thought reasoning earlier than producing the reply, we would wish to jot down further logic to parse that reasoning textual content appropriately.
Moreover, it may be troublesome to take a look at plain English prompts like these and perceive what the inputs and outputs of those programs are. DSPy solves the entire above. Let’s write the above instance utilizing DSPy.
class JokeGenerator(dspy.Signature):
"""You are a comic who tells jokes. You are all the time humorous."""
question: str = dspy.InputField()
setup: str = dspy.OutputField()
punchline: str = dspy.OutputField()
supply: str = dspy.OutputField()
joke_gen = dspy.Predict(JokeGenerator)
joke_gen.set_lm(lm=dspy.LM("openai/gpt-4.1-mini", temperature=1))
consequence = joke_gen(question="Write a joke about AI")
print(consequence)
print_on_a_tshirt(consequence.punchline)
This strategy offers you structured, predictable outputs that you would be able to work with programmatically, eliminating the necessity for regex parsing or error-prone string manipulation.
Dspy Signatures explicitly makes you outline what the inputs to the system are (“question” within the above instance), and the outputs to the system (setup, punchline, and supply) in addition to their data-types. It additionally tells the LLM the order during which you need them to be generated.

The dspy.Predict
factor is an instance of a DSPy Module. With modules, you outline how the LLM converts from inputs to outputs. dspy.Predict
is essentially the most fundamental one – you possibly can move the question to it, as in joke_gen(question="Write a joke about AI")
and it’ll create a fundamental immediate to ship to the LLM. Internally, DSPy simply creates a immediate as you possibly can see under.
As soon as the LLM responds, DSPy will create Pydantic BaseModel
objects that carry out automated schema validation and ship again the output. If errors happen throughout this validation course of, DSPy routinely makes an attempt to repair them by re-prompting the LLM—thereby considerably lowering the chance of a program crash.

One other frequent theme in context engineering is Chain of Thought. Right here, we wish the LLM to generate reasoning textual content earlier than offering its ultimate reply. This enables the LLM’s context to be populated with its self-generated reasoning earlier than it generates the ultimate output tokens.
To do this, you possibly can merely substitute dspy.Predict
with dspy.ChainOfThought
within the instance above. The remainder of the code stays the identical. Now you possibly can see that the LLM generates reasoning earlier than the outlined output fields.
Multi-Step Interactions and Agentic Workflows
The most effective a part of DSPy’s strategy is the way it decouples system dependencies (Signatures
) from management flows (Modules
), which makes writing code for multi-step interactions trivial (and enjoyable!). On this part, let’s see how we will construct some easy agentic flows.
Sequential Processing
Let’s remind ourselves about one of many key elements of Context Engineering.
It’s a holistic strategy that breaks up a bigger downside into a number of subproblems
Let’s proceed with our joke technology instance. We are able to simply separate out two subproblems from it. Producing the thought is one, making a joke is one other.

Let’s have two brokers then — the primary Agent generates a joke concept (setup and punchline) from a question. A second agent then generates the joke from this concept.
Every agent might be of acceptable functionality and measurement relying on the complexity of the duty
We’re additionally operating the primary agent with gpt-4.1-mini
and the second agent with the extra highly effective gpt-4.1
.
Discover how we wrote our personal dspy.Module
referred to as JokeGenerator
. Right here we use two separate dspy modules – the query_to_idea
and the idea_to_joke
to transform our authentic question to a JokeIdea
and subsequently right into a joke (as pictured above).
class JokeIdea(BaseModel):
setup: str
contradiction: str
punchline: str
class QueryToIdea(dspy.Signature):
"""Generate a joke concept with setup, contradiction, and punchline."""
question = dspy.InputField()
joke_idea: JokeIdea = dspy.OutputField()
class IdeaToJoke(dspy.Signature):
"""Convert a joke concept right into a full comic supply."""
joke_idea: JokeIdea = dspy.InputField()
joke = dspy.OutputField()
class JokeGenerator(dspy.Module):
def __init__(self):
self.query_to_idea = dspy.Predict(QueryToIdea)
self.idea_to_joke = dspy.Predict(IdeaToJoke)
self.query_to_idea.set_lm(lm=dspy.LM("openai/gpt-4.1-mini"))
self.idea_to_joke.set_lm(lm=dspy.LM("openai/gpt-4.1"))
def ahead(self, question):
concept = self.query_to_idea(question=question)
joke = self.idea_to_joke(joke_idea=concept.joke_idea)
return joke
Iterative Refinement
You can too implement iterative enchancment the place the LLM displays on and refines its outputs. For instance, we will write a refinement module whose context is the output of a earlier LM, and it should act as a suggestions supplier. The primary LM can enter this suggestions and iteratively enhance its response.

iteratively enhance the ultimate joke. (Supply: Creator)
Conditional Branching and Multi-Output Techniques
The brokers are related with management flows, and we orchestrate precisely how data flows via our system
Typically you need your agent to output a number of variations, after which choose one of the best amongst them. Let’s have a look at an instance of that.
Right here we’ve got first outlined a joke decide – it inputs a number of joke concepts, after which picks the index of one of the best joke. This joke is then handed into the following part.
num_samples = 5
class JokeJudge(dspy.Signature):
"""Given an inventory of joke concepts, you will need to decide one of the best joke"""
joke_ideas: listing[JokeIdeas] = dspy.InputField()
best_idx: int = dspy.OutputField(
le=num_samples,
ge=1,
description="The index of the funniest joke")
class ConditionalJokeGenerator(dspy.Module):
def __init__(self):
self.query_to_idea = dspy.ChainOfThought(QueryToIdea)
self.decide = dspy.ChainOfThought(JokeJudge)
self.idea_to_joke = dspy.ChainOfThought(IdeaToJoke)
async def ahead(self, question):
# Generate a number of concepts in parallel
concepts = await asyncio.collect(*[
self.query_to_idea.acall(query=query)
for _ in range(num_samples)
])
# Choose and rank concepts
best_idx = (await self.decide.acall(joke_ideas=concepts)).best_idx
# Choose finest concept and generate ultimate joke
best_idea = concepts[best_idx]
# Convert from concept to joke
return await self.idea_to_joke.acall(joke_idea=best_idea)
Device Calling
LLM purposes typically have to work together with exterior programs. That is the place tool-calling steps in. You’ll be able to think about a software to be any Python operate. You simply want two issues to outline a Python operate as an LLM software:
- An outline of what the operate does
- An inventory of inputs and their information varieties

Let’s see an instance of fetching information. We first write a easy Python operate, the place we use Tavily. The operate inputs a search question and fetches latest information articles from the final 7 days.
consumer = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
def fetch_recent_news(question: str) -> str:
"""Inputs a question string, searches for information and returns high outcomes."""
response = tavily_client.search(question, search_depth="superior",
matter="information", days=7, max_results=3)
return [x["content"] for x in response["results"]]
Now let’s usedspy.ReAct
(or the REasoning and ACTing). The module routinely causes concerning the person’s question, decides when to name which instruments, and incorporates the software outcomes into the ultimate response. Doing that is fairly simple:
class HaikuGenerator(dspy.Signature):
"""
Generates a haiku concerning the newest information on the question.
Additionally create a easy file the place you save the ultimate abstract.
"""
question = dspy.InputField()
abstract = dspy.OutputField(desc="A abstract of the newest information")
haiku = dspy.OutputField()
program = dspy.ReAct(signature=HaikuGenerator,
instruments=[fetch_recent_news],
max_iters=2)
program.set_lm(lm=dspy.LM("openai/gpt-4.1", temperature=0.7))
pred = program(question="OpenAI")
When the above code runs, the LLM first causes about what the person needs and which software to name (if any). Then it generates the title of the operate and the arguments to name the operate.
We name the information operate with the generated args, execute the operate to generate the information information. This data is handed again into the LLM. The LLM comes to a decision whether or not to name extra instruments, or “end”. If the LLM causes that it has sufficient data to reply the person’s authentic request, it chooses to complete, and generate the reply.
Brokers can take actions whereas producing responses. Every motion the agent can take ought to be nicely outlined so the LLM can work together with it via reasoning and appearing.
Superior Device Utilization — Scratchpad and File I/O
An evolving customary for contemporary purposes is to permit LLMs entry to the file system, permitting them to learn and write information, transfer between directories (with acceptable restrictions), grep and search textual content inside information, and even run terminal instructions!
This sample opens a ton of potentialities. It transforms the LLM from a passive textual content generator into an energetic agent able to performing advanced, multi-step duties instantly inside a person’s surroundings. For instance, simply displaying the listing of instruments accessible to Gemini CLI will reveal a brief however extremely highly effective assortment of instruments.

A fast phrase on MCP Servers
One other new paradigm within the area of agentic programs are MCP servers. MCPs want their very own devoted article, so I received’t go over them intimately on this one.
This has shortly develop into the industry-standard approach to serve specialised instruments to LLMs. It follows the traditional Shopper-Server structure the place the LLM (a consumer) sends a request to the MCP server, and the MCP server carries out the requested motion, and returns a consequence again to the LLM for downstream processing. MCPs are good for context engineering particular examples since you possibly can declare system immediate codecs, assets, restricted database entry, and many others, to your software.
This repository has a great list of MCP servers that you would be able to examine to make your LLM purposes join with all kinds of purposes.
Retrieval-Augmented Technology (RAG)
Retrieval Augmented Technology has develop into a cornerstone of recent AI software improvement. It’s an architectural strategy that injects exterior, related, and up-to-date data into the Massive Language Fashions (LLMs) that’s contextually related to the person’s question.
RAG pipelines encompass a preprocessing and an inference-time section. Throughout pre-processing, we course of the reference information corpus and put it aside in a queryable format. Within the inference section, we course of the person question, retrieve related paperwork from our database, and move them into the LLM to generate a response.
The data accessible to the brokers can come from a number of sources – exterior database with Retrieval-Augmented Technology (RAG), software calls (like net search), reminiscence programs, or traditional few-shot examples.
Constructing RAGs is sophisticated, and there was lots of nice analysis and engineering optimizations which have made life simpler. I made a 17-minute video that covers all of the features of constructing a dependable RAG pipeline.
Some sensible ideas for Good RAG
- When preprocessing, generate further metadata per chunk. This may be so simple as “questions this chunk solutions”. When saving the chunks to your database, additionally save the generated metadata!
class ChunkAnnotator(dspy.Signature):
chunk: str = dspy.InputField()
possible_questions: listing[str] = dspy.OutputField(
description="listing of questions that this chunk solutions"
)
- Question Rewriting: Straight utilizing the person’s question to do RAG retrieval is usually a foul concept. Customers write fairly random issues, which can not match the distribution of textual content in your corpus. Question rewriting does what it says – it “rewrites” the question, maybe fixing grammar, spelling errors, contextualizes it with previous dialog, and even provides further key phrases that make querying simpler.
class QueryRewriting(dspy.Signature):
user_query: str = dspy.InputField()
dialog: str = dspy.InputField(
description="The dialog thus far")
modified_query: str = dspy.OutputField(
description="a question contextualizing the person question with the dialog's context and optimized for retrieval search"
)
- HYDE or Hypothetical Doc Embedding is a kind of Question Rewriting system. In HYDE, we generate a synthetic (or hypothetical) reply from the LLM’s inner information. This response typically comprises vital key phrases that attempt to instantly match with the solutions database. Vanilla question rewriting is nice for looking out a database of questions, and HYDE is nice for looking out a database with solutions.

- Hybrid search is nearly all the time higher than purely semantic or purely keyword-based search. For semantic search, I’d use cosine similarity nearest neighbor search with vector embeddings. And for semantic search, use BM25.
- RRF: You’ll be able to select a number of methods to retrieve paperwork, after which use reciprocal rank fusion to mix them into one unified listing!

- Multi-Hop Search is an choice to think about as nicely should you can afford further latency. Right here, you move the retrieved paperwork again into the LLM to generate new queries, that are used to conduct further searches on the database.
class MultiHopHyDESearch(dspy.Module):
def __init__(self, retriever):
self.generate_queries = dspy.ChainOfThought(QueryGeneration)
self.retriever = retriever
def ahead(self, question, n_hops=3):
outcomes = []
for hop in vary(n_hops): # Discover we loop a number of instances
# Generate optimized search queries
search_queries = self.generate_queries(
question=question,
previous_jokes=retrieved_jokes
)
# Retrieve utilizing each semantic and key phrase search
semantic_results = self.retriever.semantic_search(
search_queries.semantic_query
)
bm25_results = self.retriever.bm25_search(
search_queries.bm25_query
)
# Fuse outcomes
hop_results = reciprocal_rank_fusion([
semantic_results, bm25_results
])
outcomes.prolong(hop_results)
return outcomes
- Citations: When asking LLM to generate responses from the retrieved paperwork, we will additionally ask the LLM to quote references to the paperwork it discovered helpful. This enables the LLM to first generate a plan of the way it’s going to make use of the retrieved content material.
- Reminiscence: In case you are constructing a chatbot, you will need to work out the query of reminiscence. You’ll be able to think about Reminiscence as a mixture of Retrieval and Device Calling. A widely known system is the Mem0 system. The LLM observes new information and calls instruments to determine if it wants so as to add or modify its present recollections. Throughout question-answering, it retrieves related recollections utilizing RAG to generate solutions.

Greatest Practices and Manufacturing Concerns
This part is just not instantly about Context Engineering, however extra about finest practices to construct LLM apps for manufacturing.
Moreover, programs have to be evaluated with metrics and maintained with observability. Monitoring token utilization, latency, and value to output high quality is a key consideration.
1. Design Analysis First
Earlier than constructing options, determine the way you’ll measure success. This helps scope your software and guides optimization choices.

- For those who can design verifiable or goal rewards, that’s one of the best. (instance: classification duties the place you could have a validation dataset)
- If not, are you able to outline features that heuristically consider LLM responses in your use case? (instance: variety of instances a particular chunk is retrieved given a query)
- If not, are you able to get people to annotate your LLM’s responses?
- If nothing works, use an LLM as a decide to guage responses. Normally, you need to set your analysis job as a comparability examine, the place the Choose receives a number of responses produced utilizing totally different hyperparameters/prompts, and the decide should rank which of them are one of the best.

3. Use Structured Outputs Nearly All over the place
At all times desire structured outputs over free-form textual content. It makes your system extra dependable and simpler to debug. You’ll be able to add validation and retries as nicely!
4. Design for failure
When designing prompts or dspy modules, ensure you all the time take into account “what occurs if issues go incorrect?”
Like several good software program, reducing down error states and failing with swagger is the best state of affairs.
5. Monitor Every little thing
DSpy integrates with MLflow to trace:
- Particular person prompts handed into the LLM and their responses
- Token utilization and prices
- Latency per module
- Success/failure charges
- Mannequin efficiency over time
Langfuse, Logfire are equally nice options.
Outro
Context engineering represents a paradigm shift from easy immediate engineering to constructing complete and modular LLM purposes.
The DSPy framework supplies the instruments and abstractions wanted to implement these patterns systematically. As LLM capabilities proceed to evolve, context engineering will develop into more and more essential for constructing purposes that successfully leverage the facility of enormous language fashions.
To look at the complete video course on which this text relies, please go to this YouTube hyperlink.
To entry the complete GitHub repo, go to:
https://github.com/avbiswas/context-engineering-dspy

References
Creator’s YouTube channel: https://www.youtube.com/@avb_fj
Creator’s Patreon: www.patreon.com/NeuralBreakdownwithAVB
Creator’s Twitter (X) account: https://x.com/neural_avb
Full Context Engineering video course: https://youtu.be/5Bym0ffALaU
Github Hyperlink: https://github.com/avbiswas/context-engineering-dspy