BuildPromptSequencesInLangChain
When you’re trying to duct-tape internal tools together with LangChain, something weird always happens right after you feel like you’ve got it—like the prompt you wrote yesterday is somehow worse today, even though literally nothing changed. If you’ve been staring at the same chain for hours wondering why your output is just ellipses or a single “Yes,” yeah, same. Here’s how I got around that—and built a few working prompt sequences for actual internal tooling (a weird CRM we built in-house + some Slack syncing stuff that still breaks once a week :P).
This isn’t theoretical. I had three Zapier triggers, one code block in a JS action step, and an OpenAI API key that decided to expire Sunday night 😑 so when LangChain saved the prompt, it looked normal, but failed silently when called. Here’s what I learned (after two days of thinking I broke the whole stack).
StartWithMockInputsInsteadOfLLMs
Before dragging any LLM into the chain, you absolutely need to know what the prompt is supposed to be doing.
Here’s an actual (embarrassing) example:
I built a chain that was supposed to take a customer complaint from Zendesk, rewrite it into a polite summary, and post it to the product Slack channel. Very basic. But the version I deployed started posting full customer names and personal email addresses. I assumed {{input}} would scrub those out. Uh, nope.
What I now do every time:
– Inject a plain stub input like: “Customer reports issue loading report. Error code 403 shown at 3:12pm.”
– Hardcode different variables before connecting live data.
– Run the chain in a local Python notebook or Streamlit block first.
Here’s the thing: LangChain doesn’t care if your inputs make sense. It’ll combine a half-broken prompt template with poor variable naming and still send it to OpenAI. You have to stop that by faking inputs locally first.
Also, if you’re using a SequentialChain, and you’re missing even one expected key in an earlier step, the whole thing executes quietly but gives wrong final output. Like, my re-summary step expected “complaint_summary” from an earlier format step—but I had misspelled it as “complaint_summry”. Zero errors, just silent bad results. 🙁
UseALoggerAndNotJustPrint
Printing is fine when debugging solo, but once you send this into production or let even your own CPO touch it through a webhook, you’re going to regret not logging every step. I started using the `logging` module and writing every chain step’s input/output to a text file with timestamps. It’s not elegant. It’s a lifesaver.
A sample code snippet I actually copy-pasted between projects:
“`python
import logging
logging.basicConfig(filename=’chain_debug.log’, level=logging.DEBUG)
def log_step(step_name, input_data, output_data):
logging.debug(f”[{step_name}] Input: {input_data}”)
logging.debug(f”[{step_name}] Output: {output_data}”)
“`
Then you use that in every `run()` call of your chain pieces.
Why not just use LangChain built-in tracing or memory visualization? Because:
1. It’s slow, and doesn’t work well with over-complicated tools.
2. If your API call fails or times out (OpenAI finicky limit stuff), you don’t get the log trace.
Trust me—text files are better than buried tracing sessions that need five clicks to find.
ParameterNamesWillBreakYourFlow
LangChain is super picky about parameter names.
Here’s the pit I fell into: the LLMChain expects an input parameter called `input`, but my code used `question` for the QA step. I didn’t catch it because the doc example I skimmed was using `question`, and I had copy-pasta’d the whole thing late at night. When the chain ran—blank output again. No crash. Just… blank.
So now I always double-check the actual keys my templates require. Even better, I use:
“`python
chain.input_keys
chain.output_keys
“`
Which lets you see what it thinks it needs.
Something small, but I probably wasted an hour running in circles trying different prompt format wrappers, when actually I had just not named it correctly.
Also, avoid using keys named `text` or `response` unless you absolutely have to. Way too easy to overwrite them accidentally in intermediate steps.
DynamicPromptsBreakFasterThanExpected
I know dynamic prompt templates feel flexible. I’ve used `PromptTemplate.from_template()` with string injection via `**kwargs`, and I thought it was slick. Then my designer updated a Notion doc that fed into the prompt copy, and the template broke because a random newline got inserted:
“Hi team!! Here’s a problem 👇
Server crash logs attached…”
Guess what? The double newline and emoji broke the parser. LLMs don’t care, but Python’s formatting insertion threw unexpected errors whenever `”{input}”` got passed a multiline string.
Workaround:
– Always sanitize real-world inputs. I literally use `.replace(‘\n’, ‘ ‘)` on all incoming fields now.
– Avoid using pasted Notion or Slack text raw unless you’re 100% in control of that source. They love to sneak in rich text formatting as control characters that mess up the template 🌪
– Add `repr()` temporarily during debugging to spot the weird whitespace early.
SequentialChainsHateMissingKeys
I spent most of a Friday building this beautiful SequentialChain where three LLMChains would run one after another:
1. Extract a topic + issue from user feedback
2. Generate a support response
3. Create a Slack message
It worked… in theory. But in practice it would skip the response entirely if the first chain didn’t return exactly the right key.
Turns out if your output from step 1 is just `{‘topic’: ‘Access issue’}`, and step 2 expects both `topic` AND `original_text`, you have to explicitly carry forward the first input or re-pass the original manually. LangChain doesn’t infer that unless you tell it.
What it looked like:
“`python
SequentialChain(
chains=[extract_chain, response_chain, slack_chain],
input_variables=[“feedback_text”],
output_variables=[“formatted_slack_msg”]
)
“`
What fixed it:
“`python
SequentialChain(
chains=[extract_chain, response_chain, slack_chain],
input_variables=[“feedback_text”],
output_variables=[“formatted_slack_msg”],
verbose=True,
memory=None # Don’t keep implicit history
)
“`
And ensuring every intermediate output had required fields passed forward using custom logic. It’s tedious. But the difference between “works in test” and “actually deployable.”
StopChainingBeforeYouUnderstandThePrompt
Final thought: most issues aren’t in LangChain config. They’re in how unclear your prompt is.
The classic mistake is trying to build a full toolchain before you even know what the LLM should output. I kept trying to jam new tools into an internal team triage workflow, but the prompt kept returning generic nonsense like “Thank you for your feedback. We’ll look into it.” No useful info extracted. No categories.
When I paused and just played with five versions of the prompt using raw `openai.ChatCompletion.create(…)` in a REPL, I realized it wasn’t a chaining issue at all—the prompt just sucked. So yeah. If your results garbage out, don’t start coding the chain. Start rewriting your prompt, manually, until it spits out what you want.
Then you can bolt on LangChain—after it actually answers the question you think it’s supposed to.
¯\_(ツ)_/¯